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Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Valentina Camomilla, Elena Bergamini, Silvia Fantozzi and 1 more

Sensors · 2018 · Review · CC BY

Abstract

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 18, 2026. Paragraph numbers are ours, so an answer can point at one.

1.1. Background

1A successful coaching outcome can be supported by useful and timely feedback to the athlete targeting performance defects. Systematic, objective, and reliable performance monitoring and evaluation, performed by means of qualitative and quantitative analyses of performance variables can reinforce the link between research and coaching practice, especially in elite sports. An alternative to classical laboratory-based assessment is the use of magneto-inertial sensors that can monitor performance without hindering it and with no space limitation or cumbersome setup procedure. These sensors measure physical quantities related to the motion of a body and their measurements can be used to estimate temporal, kinematic and dynamic parameters. New generation sensors permit executing sport tasks during training and performance, are portable, affordable, easy-to-use, and may provide real-time feedback. Recently, the use of such sensors has been revised in swimming, running, and strength and ballistic assessment. Their partnership with Global Positioning System (GPS) technology has been highlighted for use in team sports. In 2015, a systematic review provided a general overview of the dissemination of this technology through different sports disciplines. However, they excluded papers using only sensors mounted on equipment or dealing with non-sport-specific movements (e.g., running). Moreover, the fast expansion of this topic makes any new review quickly outdated. The current systematic review identifies and evaluates updated evidence to answer the following questions: (i) In which sport domains are magneto-inertial sensors currently adopted for athlete performance evaluation? (ii) Are sensor-based parameters used to assess motor capacity (i.e., the highest possible level of functioning of an athlete in exerting maximal velocity or strength in a standardized environment) and analyse the technique of the athlete? (iii) Can sensors be feasibly used to perform match analysis and assess physical demands during training sessions/competitions? (iv) What is currently the potential for the use of inertia-based instrumentation in the field sport setting?

1.2. Technology, Requirements and Parameters for Wearable Motion Analysis in Sports

2In this section, the necessary background is provided about (i) wearable inertial sensor technology applied in sports to support athlete movement analysis; (ii) requirements to guarantee their accurate use in sport applications; (iii) sensor derived parameters for sport performance assessment.

1.2.1. Gyroscope, Accelerometer and Magnetic Sensors

3The advent of Micro-Electro-Mechanical Systems (MEMS) technology has allowed for the manufacture of gyroscope, accelerometer, and magnetic sensors that are highly miniaturized, relatively low-cost, and power efficient.

4MEMS gyroscope, accelerometer and magnetic sensors quantify angular velocity, the sum of gravitational and inertial linear accelerations, and the local magnetic field vector components, about and along their sensing axes, respectively. Single-, dual- and tri-axis sensors exist. In the latter case, the three sensing axes are mutually orthogonally mounted, and the aforementioned physical quantities are measured with respect to a Cartesian coordinate system.

5The quantities measured by the three sensors can also be used to obtain information about the orientation of a body. However, when the orientation is provided by only one type of sensor at a time, the orientation accuracy is often inadequate for specific applications. Therefore, to improve the accuracy of 2D orientation estimation, gyroscopes and accelerometers are commonly combined in an Inertial Measurement Unit (IMU), where the term “inertial” is used because both sensors exploit the principle of inertia to provide either angular velocities or accelerations. When 3D orientation is required, magnetic sensors are also embedded within the Magnetic and Inertial Measurement Unit, widely known by the acronym MIMU (or Inertial and Magnetic Measurement Unit, IMMU, or Inertial and Magnetic Measurement System, IMMS). When IMUs and MIMUs are used, the unit’s orientation is commonly provided as an output of the device, as estimated by combining the data measured by two or three sensors, through means of sensor fusion algorithms. The principles underlying these algorithms, together with a list of parameters that can be derived from IMU and MIMU measurements are presented in Section 1.2.3.

1.2.2. Requirements for Measuring Human Movements in Sport

6Several gyroscope, accelerometer, and magnetic sensors, as well as IMUs and MIMUs, have been marketed in recent years for use in human movement analysis. The characteristics of these devices in terms of full-range scale, sampling frequency, power consumption, and portability change considerably according to the application and technology used for data transmission (cabled or wireless). When sporting applications are targeted, care must be taken in order to properly select the measurement system, according to the characteristics of the selected motor task and to the variables that are estimated (e.g., accuracy requirements for trajectory determination in sports). This aspect ranked first in the identification of user requirements for coaches, followed by the request for repeatability, understandable and communicable feedback to athletes, real-time results, suitability for multi-athlete analysis, and non-interference with the athlete or coach. In general, the system used should also be small and light (portable, suitable for in-field use); wearable (wireless); easy and quick to set up and analyse; usable on any surface and in any environmental condition (including under water through hermetic sealing and limited drag, if swimming applications are targeted); power efficient (in case long-time monitoring is required, e.g., marathon monitoring); and have adequate full-range scale and sampling frequency. The latter should be determined according to the motor task, parameters of interest, and measurement system, being a minimum of two times the highest frequency in the signal of interest (according to the Nyquist–Shannon sampling theorem), to minimise the loss of relevant information.

7Besides the characteristics of the selected device, users must be aware that the signals derived from these sensors are affected by different sources of error which, even if compensated for, currently limit the possible range of applications. Among them is gyroscope and accelerometer bias, caused by changes in the MEMS physical properties and leading to drift errors that increase linearly or quadratically over time when the measured signal is integrated once (to obtain orientation) or twice (for position estimation), respectively. Secondly, the oscillations between body and device, i.e., soft tissue artefacts, compete with drift as the most crucial source of error. The fixing technique may limit the oscillations as their compensation is essential. However, as soft tissue artefacts are motor task- and subject-dependent, as well as sensitive to the site and method of the unit attachment and to unit removal and replacement, their compensation is very critical. Moreover, they can be assumed to be larger during explosive tasks; to analyse such tasks, the device mass should be limited to a minimum. Thirdly, ferromagnetic disturbances (particularly indoors) may come from ferromagnetic objects or electrical appliances like, for example, water pumps under the pool. They alter the magnitude and direction of the measured magnetic field vector, thus causing errors in the estimated orientation.

1.2.3. Sensor Derived Parameters

8Gyroscope, accelerometer, and magnetic sensor data can be used to estimate different parameters. These could be based on the detection of features in the measured signals or on more sophisticated processing techniques, which, for example, enable information provided by two or more sensors to be combined. Sensor derived parameters can be divided into three main categories: temporal, kinematic, and dynamic.

Temporal Parameters

9Temporal parameters are typically obtained by detecting features (e.g., maxima, minima or slope changes) in the measured angular velocity or acceleration signals. Some examples of these parameters are: (i) time instants related to the beginning or end of a movement, foot or hand contact, ball release or other events of interest; (ii) phase identification and segmentation of sport tasks using the time instants; (iii) time intervals, such as stride/step/stance duration in locomotion or stroke duration in swimming; and (iv) frequency in cyclic movements.

Kinematic Parameters

10Kinematic parameters refer to both angular and linear forms of position, velocity and acceleration. In particular, the estimation of the 3D orientation of a MIMU with respect to an inertial reference frame is of fundamental importance as it is essential when obtaining either the absolute orientation of body segments or sports equipment (e.g., rackets, clubs, skis), or the orientation of a body segment relative to another (joint kinematics). Moreover, it is necessary to set apart the gravitational and inertial components of the linear accelerations, in order to estimate the linear kinematic parameters, such as MIMU position or linear velocity.

11The 3D orientation of a MIMU may be estimated by numerical time-integration of the differential kinematic equations relating the time derivatives of the orientation parameters to the angular velocity provided by the gyroscope. The accuracy of this numerical integration, however, is hindered by errors that increase with time due to gyroscope drift. Moreover, the initial conditions of the integration process must be determined. To manage these problems, the signals provided by the accelerometric and magnetic (aiding) sensors are used under the following hypotheses: (i) the accelerometers measure only the gravity vector, true only when the MIMU moves at constant speed or is stationary; (ii) both the gravitational and magnetic field vectors are uniform and constant within the measurement volume, which, as mentioned above (Section 1.2.2), is not necessarily the case for the magnetic field vector. Therefore, the information provided by the three sets of sensors is combined within a sensor fusion framework with the aim of proficiently exploiting all the available information. Examples of sensor fusion approaches are stochastic filtering, often implemented in the form of an Extended Kalman Filter and complementary filtering.

12Once orientation is obtained, joint kinematics can be estimated as the relative orientation between adjacent body segments. Its computation requires the definition of a multibody kinematic model, together with its anatomical calibration, and the determination of the reference “zero” joint configuration. Among other angular kinematic parameters, the angular velocity is often exploited as it is directly measured by gyroscopes, while the angular acceleration must be obtained through numerical differentiation of the measured velocity. Like the integration process, computing the derivative of a discrete signal entails errors resulting from the differentiation method and the type of representation used for numbers (precision level).

13As for linear kinematic parameters, in theory, the measured linear acceleration can be numerically integrated, once amended by its gravitational component, to obtain an estimate of the linear velocity and, if further integrated, the displacement (trajectory). Again, the initial conditions of this process are required, and drift errors may occur and propagate due to low-frequency gyroscope drift. Moreover, the removal of the gravitational component is prone to errors related to the orientation calculation. For these reasons, model-based approaches are often required and the accurate estimation of linear position from MIMUs still represents a challenging and time-consuming exercise.

14It is noteworthy to specify that centre of mass (CoM) parameters estimated from the acceleration of a single sensor positioned on the lower trunk, nearby the position of the CoM in the static upright posture, is an approximation that does not take into account the dynamics of all the body segments, specifically the lower and upper limbs. Still, this information can be useful in in-field sport performance evaluation without requiring cumbersome setup.

Dynamic Parameters

15Like kinematic quantities, dynamic parameters refer to both linear and angular variables. Although, in theory, vertical/leg stiffness, external forces, joint forces, moments, and powers can be estimated from MIMU measurements, the accuracy and reliability of these parameters are contingent on the accurate estimation of kinematic variables (e.g., temporal parameters, orientation, and position). Additionally, the embedding of measures of external forces in hybrid inverse dynamic methods is recommended to reduce errors of pure inverse dynamic estimates of joint loads.

2.1. Inclusion and Exclusion Criteria

16Using the PICOS framework (which focuses on Population, Intervention, Comparison, Outcomes, and Study design), the selection criteria and search strategy were designed to answer the above-mentioned research questions. Full papers published in English, including studies using MIMUs and IMUs to analyse motor tasks of interest in a sport context, performed by recreational or elite athletes, were considered. During the final screening, selections were made based on the relevance of the identified sources to the assessment of maximum strength and ballistic performance, technique related parameters, and intensity measures for match analysis. The other inclusion and exclusion criteria are described in Table 1.

2.2. Search Strategy

17The review of the literature was performed by selecting articles from Web of Science, Scopus, Pubmed, and Sport Discus databases, published in English, until 12 April 2017. Keywords were selected to define the measurement instruments, the sport activity performed, the participant tested and to exclude studies dealing with patients, risk of injury, and the assessment of physical activity or energy expenditure (the Boolean research strategy used is reported in Table 2). A keyword search was performed to match words in the title, abstract, and keyword fields. Electronic searches were performed by one author (E.B.); references and abstracts were stored alphabetically into a separate worksheet. Additional relevant papers were thereafter selected by examining the reference lists of selected papers.

2.3. Review Process

18Conference proceedings, theses, and duplicate journal references due to searches in different electronic databases were removed first. During the subsequent screening, selections were based on the relevance of the identified sources to the inclusion criteria. Two independent reviewers (E.B. and V.C.) evaluated titles and abstracts of the retrieved papers for inclusion. A full text evaluation was then performed if the title and abstract failed to provide adequate information. Any disagreement between the two reviewers was resolved by a consensus meeting of all authors.

3.1. Paper Selection and Identification

19The duplicate removal yielded 2040 citations (Figure 1). One hundred and eighty-three papers were included based on title and abstract, whereas the full text was reviewed for 119 papers. Three papers were directly included, while 95 papers were discussed during multiple consensus meetings, leading to the inclusion of a further 23 papers. Several studies were excluded for not involving athletes. Fourteen review papers were retrieved from the search and were analysed separately, along with nine additional reviews cited by the selected papers and reviews. Screening of the selected papers and the 23 reviews produced 77 additional papers. As a result, 286 studies were identified for inclusion in the systematic review (Figure 1) and analysed along with the 23 reviews in the following sections. Details of the papers’ aims, performance evidence, sport, motor task, participants, setting, parameters, device characteristics and fixing are reported in the Supplementary Material.

3.2. Journals and Years

20The papers included in the systematic review (286 papers) and the reviews (23 papers), (total: 309 papers) were published in 88 different journals, 71% of which appeared in 14 journals, each publishing at least five papers (Figure 2). Trends of publication along the years are also shown (Figure 2).

3.3. Participants, Setting, Sport, Motor Task

21The papers included in the systematic review (286 papers) analysed elite (declared as such, professionals, or national to international level, 38% of the papers), sub-elite (competitive, university, highly trained, and experienced athletes, 21%) and recreational athletes (26%), while multiple experience levels were included in 8% of the papers, and 7% were not specified. Three studies involved Paralympic athletes and two involved wheelchair junior athletes. Studies analysing athletes in the field during training or simulated training comprised 62% of the total analysed, with 7% undertaken during competition, and 28% of the studies performed in laboratory settings. When multiple settings were included in a single study, the setting closest to performance was counted.

22As detailed in Table 3, cyclic and team sports comprised 35% and 25% of studies analysed, respectively. The possibility of measuring movement outdoors created opportunities in winter sports (aerial skiing, alpine skiing, cross-country skiing, ski jumping, ski mountaineering, snowboarding: 8%), water sports (diving, swimming, rowing, kayaking: 16%), team sports (American football, Australian football, baseball, basketball, contact sports, cricket, field hockey, Gaelic football, handball, ice hockey, netball, rugby, soccer, softball, volleyball, wheelchair basketball: 25%), and other outdoor activities (sprint and distance running, cycling, golf, tennis, rock climbing, shot-put, roller skiing, skateboarding: 27%).

23Regarding the specific motor tasks investigated, almost all belonged to the following broad gross motor skill classifications:

24Locomotion:aerial movements (diving trampoline jumps, half-pipe snowboard, front/back somersaults)agility runs (change of direction, side-cuts, turning)bicycling (indoor or outdoor track, jumping)horse riding (trot, gallop, circles, change of directions)jumping (with and without counter movement, with one or two legs, drop landing, drop jump, stop jump, jump header, moving header, standing short\long jump, springboard)climbingswimming (starts, front crawl, backstroke, breaststroke, butterfly, freestyle, turns)roller skating and skateboardingrowing and paddling (single and double scull rowing, kayak propulsion)running (treadmill and over ground, distance and sprint running, hurdles)skiing and snowboarding (giant slalom, downhill, cross country runs, ski jumping, uphill ski mountaineering, roller skiing)wheelchair sprint and turns.

25aerial movements (diving trampoline jumps, half-pipe snowboard, front/back somersaults)

26agility runs (change of direction, side-cuts, turning)

27bicycling (indoor or outdoor track, jumping)

28horse riding (trot, gallop, circles, change of directions)

29jumping (with and without counter movement, with one or two legs, drop landing, drop jump, stop jump, jump header, moving header, standing short\long jump, springboard)

30climbing

31swimming (starts, front crawl, backstroke, breaststroke, butterfly, freestyle, turns)

32roller skating and skateboarding

33rowing and paddling (single and double scull rowing, kayak propulsion)

34running (treadmill and over ground, distance and sprint running, hurdles)

35skiing and snowboarding (giant slalom, downhill, cross country runs, ski jumping, uphill ski mountaineering, roller skiing)

36wheelchair sprint and turns.

37Collisions, takedowns, or weightlifting:tackling and sustaining physical collisionstakedownsweightlifting, entailing the use of upper or lower limbs (snatch lift, back squat, pull/throw on bench press).

38tackling and sustaining physical collisions

39takedowns

40weightlifting, entailing the use of upper or lower limbs (snatch lift, back squat, pull/throw on bench press).

41Throwing, aiming, or hitting:kicks (soccer in step kick, karate front kick)trunk rotations (golf and baseball swing, ice hockey slap shot, baseball bat swing)overarm movements (tennis serve/volley/strokes, pitching, cricket bowling, javelin throwing, shot put) sidearm and underarm movements (air pistol shots, boxing straight punch, karate jabs/crosses/hooks/upper-cuts, fencing lunge and touche, bowling ball throw, golf putt).

42kicks (soccer in step kick, karate front kick)

43trunk rotations (golf and baseball swing, ice hockey slap shot, baseball bat swing)

44overarm movements (tennis serve/volley/strokes, pitching, cricket bowling, javelin throwing, shot put) sidearm and underarm movements (air pistol shots, boxing straight punch, karate jabs/crosses/hooks/upper-cuts, fencing lunge and touche, bowling ball throw, golf putt).

3.4. Technique Analysis, Intensity Measures for Match Analysis, Motor Capacity Assessment, and Activity Classification

45In the revised literature, the primary focus was technique analysis (55.5%), followed by match analysis (20.7%), and capacity assessment (17.3%), with few papers dealing with activity classification (6.5%). Papers are presented as classified into these four categories and referenced in more than one category when appropriate.

3.4.1. Technique Analysis (163 Papers)

46Spatio–temporal parameters (phase identification, foot contact timings, stride/step/stroke number, duration, frequency), linear and angular kinematic variables (position/orientation, mean or instantaneous linear/angular velocity, acceleration), and linear and angular dynamic parameters (leg stiffness, joint forces, moments, powers), listed in Table 4, were estimated based on the many possible configurations of sensor fixing on athletes and/or sport equipment depicted in Figure 3.

Spatio–Temporal Parameters

47For non-cyclic tasks, temporal parameters were identified to measure critical temporal events (blade–puck contact time in ice hockey, cricket bowling and, more specifically, ball release), or detect task phases and critical events (in ski jumping, half-pipe snowboard, bowling, baseball swing, instep kick, karate front kick, diving trampoline jumps, artistic gymnastics springboard jumps, golf, javelin throw, soccer turning manoeuvres, swimming tumble turn and start and cricket bowling).

48For cyclic tasks, temporal identification can entail the simple identification of a stride/step/stroke event to measure their number, rate, duration, or the more complex identification of specific events within the cycle to subdivide it into different phases. Stroke duration and/or number has been identified in kayaking, single sculler and multi-person rowing, and swimming. For swimming, sensor placement at the trunk/limbs/head was used to provide either arm strokes (for front crawl), kick strokes (for front crawl and freestyle), or generic strokes (for front crawl, butterfly, breaststroke). Stride and step frequency/duration have been assessed during running, skating, and in the run up of cricket ball delivery. Revolution rate was characterised for a bowling ball and during cycling. Phase segmentation was performed in swimming (to identify front crawl stroke phases), in cross-country skiing and uphill mountaineering (to determine cycle rate from plant and lift-off of each ski and pole ground contact), in ice hockey skating (to determine cycle rate from initial contact to blade-off for each skate), and during running on a track (trunk placement and shank or foot placement), on a treadmill (trunk placement and shank or foot placement), and during the acceleration/maintenance phase of sprint running (trunk placement and foot placement).

49Corresponding to these temporal parameters, the average step/cycle length was obtained for arm strokes in swimming and the average stride length was derived from either measured motion and contact times (for treadmill running and track running), and dividing the average cycle velocity by cycle time (in running, cross-country skiing and uphill mountaineering).

Body Segment and Centre of Mass Kinematics

50Obtaining body segment and CoM instantaneous velocity from the integration of acceleration in the global frame entails the challenge of properly compensating for sensor drift through the design of appropriate processing techniques. These algorithms first remove gravity from acceleration data (see below for orientation estimation methods) and then integrate acceleration by the following method:using known treadmill motion information (during simulated cross-country skiing)performing the integration over a short time duration through proper segmentation of the task and using a priori information to reset the error at each cycle (running speed obtained from a shank sensor)assuming equal initial and final conditions for vertical velocity and position (during vertical jumping)segmenting the data and removing mean velocity, under the assumption that each temporal segment is at steady-state (mean velocity computed by averaging few rowing cycles with a moving window for single sculling, or according to changes in forward acceleration variance for swimming)through data fusion with GPS based velocity estimates (during running and cycling, alpine skiing, and snowboarding).

51using known treadmill motion information (during simulated cross-country skiing)

52performing the integration over a short time duration through proper segmentation of the task and using a priori information to reset the error at each cycle (running speed obtained from a shank sensor)

53assuming equal initial and final conditions for vertical velocity and position (during vertical jumping)

54segmenting the data and removing mean velocity, under the assumption that each temporal segment is at steady-state (mean velocity computed by averaging few rowing cycles with a moving window for single sculling, or according to changes in forward acceleration variance for swimming)

55through data fusion with GPS based velocity estimates (during running and cycling, alpine skiing, and snowboarding).

56Alternative to the use of measured acceleration, body segment or CoM velocity can be derived from the corresponding angular velocity and definition of a forward kinematic model (used to estimate forward speed of the tennis racket head or for full body CoM during alpine skiing). Subject specific models were also developed to estimate CoM velocity during running, based on step rate measures. The errors entailed in instantaneous velocity estimation can be partially overcome by limiting the velocity analysis to average values in cyclic sports, for example, for each swimming stroke turning action, lane, running cycle, or mean velocity in running. The average velocity of progression was also obtained from ski IMUs, removing drift and assuming zero-velocity during a portion of the ski thrust phase, in cross-country skiing and in uphill mountaineering.

57Obtaining forward horizontal and vertical CoM displacement is less sensitive than estimating velocity due to inaccuracies in the removal of gravitational acceleration and to drift errors amplified by the double integration of acceleration. Such variables have been determined under two possible conditions: either when drift errors were small with respect to the measure of interest, or when displacement was obtained with respect to a known reference position. The former applies, for example, to the assessment of large displacements from a starting position, such as during jumps when skiing, snowboarding, mountain biking (height and horizontal distance obtained using GPS data), cross-country skiing, and uphill mountaineering. Error can even become negligible when CoM global trajectories are used solely to support the interpretation of other estimated variables, as is done for external force estimates during ski racing. Six possible approaches have been proposed to derive known reference positions:subtracting the linear trend line over the analysed time period from the Euler anglesassuming that the mean value is constant for cyclic events, to derive vertical CoM excursion from peak to peak values within each cycle (while running on a track or on a treadmill)defining a body model and measuring body segment orientation with respect to a reference to obtain vertical and horizontal CoM displacements (with respect to the skis at take-off during ski jumping, with respect to the ground during a golf swing and with respect to a Global Navigation Satellite System (GNSS) antenna placed over the head during alpine skiing)starting and ending the movement in known positions, to obtain shoulder, elbow and wrist, and solely elbow trajectories during baseball pitchingthrough data fusion with GPS based position estimates, which simultaneously compensates for short-term GPS outages (during outdoor activities such as snowboarding)fusing the velocity of a root point (integral of a pelvic sensor acceleration) with the velocity estimated from the lower limb kinematics at ground contact.

58subtracting the linear trend line over the analysed time period from the Euler angles

59assuming that the mean value is constant for cyclic events, to derive vertical CoM excursion from peak to peak values within each cycle (while running on a track or on a treadmill)

60defining a body model and measuring body segment orientation with respect to a reference to obtain vertical and horizontal CoM displacements (with respect to the skis at take-off during ski jumping, with respect to the ground during a golf swing and with respect to a Global Navigation Satellite System (GNSS) antenna placed over the head during alpine skiing)

61starting and ending the movement in known positions, to obtain shoulder, elbow and wrist, and solely elbow trajectories during baseball pitching

62through data fusion with GPS based position estimates, which simultaneously compensates for short-term GPS outages (during outdoor activities such as snowboarding)

63fusing the velocity of a root point (integral of a pelvic sensor acceleration) with the velocity estimated from the lower limb kinematics at ground contact.

64CoM acceleration was also assessed to detect event timing, typically the impacts during running (from the upper body, heel and metatarsal heads, and the tibia). Impact amplitude was reported for running, landing from horizontal jumps, and for tennis racket shocks. Variations in acceleration amplitude are also considered relevant when characterising running performance, running economy (acceleration root mean squared value divided by average speed) and symmetry, as well as turning manoeuvres in soccer.

65Body segment kinematics were considered to assess various parameters: postural tremor of air pistol shooters; take off velocity and angle during standing horizontal jumps as obtained from body segment orientations; shank linear and angular velocity, angular acceleration, and antero–posterior linear acceleration during an instep kick; tibial accelerations in depth jumps; heel lift and forearm acceleration to describe running economy; jerk of hip trajectory in climbing; and segment vibrations during road and off-road cycling and, in the latter condition, with different wheels.

Body Orientation

66Body segment orientation was usually estimated by fusing data from different sensors, for example, using MIMU data alone to estimate trunk inclination during a sprint start, running on a track, during a golf swing, and during outdoor activities, such as snowboarding. Inclination of body segments and angular velocity about the cranio–caudal axis were also estimated during trampoline jumps. Pelvis orientation was dealt with in swimming and climbing. MIMU data were complemented by seat reaction forces to estimate bike and rider trunk orientation, while GPS data were added to estimate limb orientation in ski racing. Constraints specific to a task, such as known starting and ending positions, were used to improve the accuracy of upper limb orientation during running and baseball pitching. Similarly, aerodynamic constraints were embedded within a sensor fusion algorithm to estimate sacrum, thigh, and shank orientation during ski jumping. Few studies estimated the trunk rotation about a longitudinal axis: one study hypothesised that the accelerometers measure only the gravity vector, i.e., the acceleration directly provides the sensor orientation (swimming) whereas another study used numerical time-integration of gyroscope data to analyse aerial manoeuvres in half-pipe snowboarding. The latter approach was also used to obtain upper arm internal rotation angles in tennis. A pioneering application about body orientation using IMUs was given in the definition of human whole body pose during bicycle riding by considering the constraints characterising human–bicycle interaction.

67Estimating body segment orientation is also essential when removing the gravity vector from measured accelerations to assess CoM acceleration in the global frame. In this framework, drift removal was performed either through data fusion (alpine skiing) or, if all sensors were not available, under task-specific assumptions. For example, during swimming, the average axis about which the sacrum rolls was assumed to be in the forward direction or during running, yaw was assumed to be zero when moving in a straight line or, again, that the net measured acceleration of the runner was equal to gravity and that its orientation was constant during the trial.

68Joint kinematics require both a good orientation estimate and appropriate anatomical calibration. They were determined for wrist flexion (tennis serve, swimming), shoulder angular displacements (internal-external or/and abduction-adduction: tennis serve, internal–external rotations in running, flexion–extension, abduction–adduction, and internal–external rotation in swimming), elbow angular displacements (flexion–extension and pronation–supination in swimming), hip angular displacements (flexion–extension in ski jumping, flexion–extension, abduction–adduction, and internal–external rotation in cycling, external rotation in horse riding), hip angular velocities on the sagittal plane (karate front kick), knee angular displacements (flexion-extension in ski jumping, alpine skiing, cycling and snowboarding, flexion–extension, abduction–adduction, and internal–external rotation in alpine skiing), knee angular velocity on the sagittal plane (karate front kick, running), shank–ski angle on the sagittal plane (ski jumping), ankle angular displacements (plantar–dorsiflexion, inversion–eversion, and internal–external rotation in soccer specific tasks, plantar–dorsiflexion in cycling), and for all the main joints during trampoline jumps.

Object orientation and kinematics

69The estimation of these parameters entails coping with the typical sensor errors but does not entail soft tissue wobbling. Orientation and kinematics have been estimated for several activities:alpine skiing (ski inclination in a lateral direction with respect to the Earth’s fixed coordinate system, describing the skis’ steering and edging manoeuvres)baseball (bat position, orientation, linear velocity during the swing; ball’s velocity at release)bowling (ball velocity)combat sports (acceleration of the boxing bag at the time of the punch)cricket bowling (outward acceleration at ball release)cycling (roll angle, crank angle)ice hockey (puck velocity)fencing (foil speed during lounge and touche)golf (clubface orientation, head velocity, and position; clubface orientation, head velocity and acceleration)gymnastics vaulting (springboard kinematics during jump on and take-off)javelin throw (javelin kinematics during the throw)kayaking (instantaneous boat velocity)rowing (boat acceleration, orientation, velocity, forward movement and balancing, propulsion, and distance travelled, as well as boat–oar stroke angle, oar acceleration), seat position)shot-put (shot acceleration)ski jumping (ski velocity, ski horizontal and V-opening angles during jumping)softball (ball’s velocity at release)tennis (consistency of backhand, forehand, overhand swings)outdoor running (path incline)uphill mountaineering (ski slope orientation)weightlifting (barbell acceleration measured during power snatch, power clean and jerk from the rack and snatch lift).

70alpine skiing (ski inclination in a lateral direction with respect to the Earth’s fixed coordinate system, describing the skis’ steering and edging manoeuvres)

71baseball (bat position, orientation, linear velocity during the swing; ball’s velocity at release)

72bowling (ball velocity)

73combat sports (acceleration of the boxing bag at the time of the punch)

74cricket bowling (outward acceleration at ball release)

75cycling (roll angle, crank angle)

76ice hockey (puck velocity)

77fencing (foil speed during lounge and touche)

78golf (clubface orientation, head velocity, and position; clubface orientation, head velocity and acceleration)

79gymnastics vaulting (springboard kinematics during jump on and take-off)

80javelin throw (javelin kinematics during the throw)

81kayaking (instantaneous boat velocity)

82rowing (boat acceleration, orientation, velocity, forward movement and balancing, propulsion, and distance travelled, as well as boat–oar stroke angle, oar acceleration), seat position)

83shot-put (shot acceleration)

84ski jumping (ski velocity, ski horizontal and V-opening angles during jumping)

85softball (ball’s velocity at release)

86tennis (consistency of backhand, forehand, overhand swings)

87outdoor running (path incline)

88uphill mountaineering (ski slope orientation)

89weightlifting (barbell acceleration measured during power snatch, power clean and jerk from the rack and snatch lift).

90In some studies, reference values for MIMU data integration were used to estimate instantaneous velocity (GPS data for kayak analysis, video cameras for javelin kinematics during the throw).

Dynamics and Power

91The estimation of dynamic variables is rarely carried out, due to difficulty in assessing external forces. None of the analysed studies carried out a validation against dynamic measures, but some studies reported data from the literature for comparison. External forces were estimated through inverse dynamics; total aerodynamic force applied on the athlete’s body assumed as rigid, during the stable flight phase of ski jumping; hand force applied on the javelin during the acceleration phase of the throw; force and time duration of a strike on a punching bag; centrifugal force, applied outward on a skier during a turn. To estimate forces exchanged with the environment during skiing, inverse dynamics requires CoM acceleration, estimated from an articulated system of rigid bodies (using a body model). The resultant force acting on the athlete’s CoM, perpendicular to the ski table, was obtained during the take-off phase of ski jumping. For overground skiing, information about the ratio of loading between the skis is also required to resolve the indeterminacy of the inverse dynamics problem. An approximation of this ratio was obtained from pressure sensor insoles embedded into the ski boots, in order to estimate the ground reaction force applied to each foot, aerodynamic force, ski friction, power, and energy during ski racing. Joint dynamics were obtained through pure inverse dynamics for tasks entailing no exchange of forces with the environment, as is typical for open kinematic chain movements of the upper and lower limbs (elbow forces in baseball pitching and shank dynamic variables during instep kicks). For tasks entailing the exchange of forces with the environment, dynamic measurement systems and a hybrid inverse dynamics approach were used, mounting force plates on the snowboard, to obtain lower limb net joint moments during snowboarding turns. IMUs were also used to obtain vertical stiffness during running, by estimating the vertical ground reaction force during the support phase using measured contact times and the participant’s body mass.

Spectral Analysis

92The frequency content of the acceleration of body segments, usually analysed in relation to the risk of injury, was, in some cases, associated with performance in (i) runners, when investigating the association between strike patterns and shock attenuation or soft tissue vibrations; (ii) air pistol shooters, during aiming, as postural tremor is associated with performance; and (iii) cross country mountain biking, to describe the relationship between vibration mechanics and their interaction with the terrain, bicycle, and rider.

Technique Analysis Objectives

93The objectives related to technique analysis pursued with MIMUs span a broad spectrum, including

94visual inspection of kinematic data to support qualitative understanding of the technique (kayaking, running, springboard diving, weightlifting, cross-country skiing)

95pacing strategy, determination by stride/step/stroke rates assessment (detailed references in the spatio–temporal parameters section)

96assessment of inter-segment coordination (lower-limbs during the take-off extension in ski jumping, hip, chest, and wrist during baseball swings) and of inter-arm coordination (front-crawl)

97assessment of symmetry (running gait, wheelchair propulsion, horse riding) and regularity\variability (running gait, cycling)

98detection of foot strike type

99assessment of the impact of training tools and accessories during running (static stretching, wearing graduated lower-leg compression sleeves) or of caffeine supplementation during simulated field hockey

100comparison of different techniques (cross-country skiing)

101evaluation of the adherence of a movement technique to appropriate normative data (performance model) for both injury management and performance enhancement

102investigation into the mechanisms that maximise speed in multi-articular joint movements (increase in muscular efficiency vs different muscular activation patterns in fencing and karate)

103investigation into the effect on performance of varying the sports objects characteristics (e.g., effect of ball size on player reaction and racket acceleration during the tennis volley)

104use of technical information to match equipment with athlete, or in crew selection processes (kayaking)

105synthesis of athlete motion in competitive game-play by finding related action sequences in a database of reference motions using a sparse set of measurements from the performance (exemplified for tennis shots)

106differentiation between categories of athletes, based on key performance parameters and technique (elite and sub-elite for ice hockey blade–puck interaction and for air pistol shooters, and highly trained inter-collegiate and untrained runners in terms of acceleration economy)

107monitoring fatigue during running

108determine an optimal gradient and speed during uphill ski mountaineering.

109Wearable sensors have also been embedded into sport-specific feedback and coaching systems which may be used for training and race analysis, providing to the coach/athlete real-time, visual, tactile, and auditory information, as well as data to be analysed immediately. These systems must be designed to comply with user requirements (coaches, biomechanists, and athletes), reliability and sport-specificity being the most important. Acoustic and visual feedback systems have been developed both for sweep rowing and sculling, for example, performing start and steady-state analysis, under training and race conditions, based on boat velocity and stroke rate. Another promising sport for feedback use is swimming, whose performance and technique parameters, such as CoM velocity, arm strokes, distance per arm stroke, body balance, and body rotation, are assessed and provided to the coach/athlete through a user-friendly interface. Proof of concept for coaching/training systems has also been demonstrated in skiing, golf, baseball swing, shot-put, volleyball, and weightlifting.

3.4.2. Match Analysis and Load Monitoring/Physical Demands Assessment (61 Papers)

110All but one of the studies performed during competition belong to this category (18 papers), while 38 were held in the field during training. Most of the match analysis involving IMUs was carried out using Integrated Technology (IT). IT was developed to increase the sensitivity of GPS systems to abrupt changes in running velocity, direction, and to impacts, typical of most team-sports, by incorporating high-frequency (100 Hz) tri-axial accelerometers in GPS systems. Some systems (e.g., Minimax, Catapult Sports, Melbourne, Australia) also incorporate gyroscopes and magnetometers to improve tackle detection and report the direction of travel, respectively. The MIMUs’ role in the context of match analysis is, therefore, complementary to that of GPS systems (see reviews by).

111Due to the limitation of GPS systems in tracking movement patterns indoors, research based on IT use covers mainly outdoor sports, including American football, Australian rules football, field hockey, handball, Gaelic football, netball, rugby, rugby sevens, soccer, aerial skiing, and tennis. In indoor studies, training load was assessed solely using accelerometer data in badminton, basketball, mixed martial arts, tennis, and volleyball.

112The match analysis performed in team sports is most often based on the body’s CoM kinematics. This provides information on the amount of the mechanical work associated with the CoM during locomotion and contact-based fatiguing movements generated during training and/or competition. This external component of training load can be used as an activity profile measure (i.e., exercise intensity, body or workload marker). External load is generally determined using IT systems, to measure the distances covered in a variety of locomotor classifications or speed zones. To characterise the intensity of player activity in sports entailing frequent collisions, this approach must be complemented with quantification of the contact demands. Success in such sports is also contingent on the ability to tackle and tolerate physical collisions. Monitoring collisions has implications for injury risk, muscle damage, recovery, and training load manipulations, while the number, type, and magnitude of tackles, as a subset of collisions, are recognised to be crucial to match outcome. The ecological validity of tackle detection depends on the sensor type, algorithm, and sport analysed. An acceleration threshold may not be able to set apart tackles from collisions, whereas, both assessing body lean with gyroscopes and developing ad hoc algorithms to identify tackles on acceleration signals were shown to improve ecological validity. This, however, may not transfer across sports. A crucial role can also be attributed to filtering, although this role was only assessed for generic impacts, and to the assessment of individual devices, particularly for mechanical damage and signal drift errors, to prevent poor validity.

113To estimate external load, trunk acceleration has been used, although it was poorly correlated with whole body mechanical loading. The parameter commonly assumed to be proportional to external load is the mean squared instantaneous rate of change in acceleration (i.e., the jerk vector amplitude), termed PlayerLoad™ in some proprietary systems. This external load measure has acceptable within- and between-device reliability, moderate to high test–retest reliability, and convergent validity with measures of exercise intensity on an individual basis. Some studies distinguished the jerk contribution to external load along different directions or its accumulation over time. This parameter varies between sports, training modes, player position/role, etc.. For this reason, methods for quantifying external load should undergo a sport-specific validation, as done, for example, for volleyball, mixed martial arts, and Gaelic Football. External load should also be combined with internal training-load measures (e.g., session rating of perceived exertion) to monitor an individual’s response to the training dose.

114Many objectives were pursued using measures of external load, such as

115quantifying physical and technical demands for a specific sport (aerial skiing, American football, Australian football, mixed martial arts, rugby, soccer)

116assessing the effect of playing position (American football, Australian Football, handball, netball, rugby, rugby sevens, soccer), playing level (elite and sub elite: Australian Football, soccer), numerical advantage (soccer), playing time, playing standard (Australian Football, badminton, rugby), and age group (tennis)

117characterising successful teams (rugby)

118assessing the effect of match score on activity profile and skill performance (Australian football)

119determining how external load measures and skill performance contribute to coaches’ perceptions of performance (Australian football) and how these vary between matches (Australian football)

120assessing the contribution of running to external load, through a correlation analysis of distance covered (field hockey)

121assessing the number of collisions and repeated high-intensity efforts

122assessing differences between different training conditions (rugby) and with competition demands (Australian Football, mixed martial arts, netball, rugby, soccer)

123determining the best load assessment according to training mode (rugby)

124investigating agility demands of small-sided games (Australian football), and closed drills

125inferring information about internal load (soccer)

126assessing the impact of playing experience on internal load, as estimated through the external load

127assessing fatigue, from training sessions (rugby), from consecutive days of match play (tennis), and in real time (soccer).

128More sophisticated match analysis may complement physiological demands with additional information about players’ movements within their positional zones, for example, assessing an individual’s tactical behaviour with respect to players with the same role.

3.4.3. Motor Capacity Assessment (51 Papers)

129Exploring the force–velocity relationship during movements that involve complex inter-muscular coordination in a standardised setting can provide useful information when designing strength training programmes. To this aim, motor capacity standardised assessments have been developed to determine either velocity or power during the acceleration and deceleration of a constant mass, as is typical in athletic movements (iso-inertial assessment). For athletic profiling, these assessments should comprise both measures of the athlete’s maximal velocity (explosive performance, also referred to as ballistic performance) and maximal strength (possibly when lifting a set of increasing loads in classical overload exercises). Both types of assessment have been performed using MIMUs that provide instantaneous acceleration (and therefore force) data along with an integrated measurement of velocity to determine muscular power: explosive performance was assessed for the lower limbs during jumps and for the upper limbs. Force and power production capacity were quantified during maximum dynamic strength tests for the upper limbs and lower limbs. As an indirect assessment of force production capacity, static muscular stiffness has also been assessed. The accuracy and reliability of both explosive performance and strength assessments has been widely acknowledged as being dependent on data collection equipment. The MIMU based methods were not always shown to be the most accurate or reliable, but can be considered acceptable according to the subsequent application, once the level of error associated with a given configuration is considered.

130Explosive performance has been analysed using MIMUs during various vertical jumping activities, such as squat, countermovement, and rebound jumps, or landings. This data is quantified for the lower limbs in terms of jump height, maximal velocity, and peak power, eventually separating concentric and eccentric contributions. Several studies tested the accuracy, reliability, and validity of MIMUs in providing jump height or other jump related parameters, such as peak knee flexion and trunk lean during landing, take-off velocity, contact and flight time, and force and power. Upper limb ballistic performance has been analysed in terms of maximum velocity, estimated strength, peak power during explosive bench press, and also upper limb ability to elevate the centre of mass during upper limb jumps. Based on current reliability findings, all of the reviewed explosive performance assessments may be implemented and used to support different training issues: (i) to determine indices of efficiency relevant to sport-specific performance; (ii) to distinguish between athletes’ skill levels; (iii) to select the best training programmes to improve performance; (iv) to determine the optimal load for ballistic tasks performed with overloads (such as jump squats); (v) to assess explosive strength in relation to various factors (such as the athlete’s respective sport, level of engagement (recreational or professional), gender, and effect of weight machine design).

131The reliability and accuracy of velocity and power measures performed using accelerometers during maximum dynamic strength assessments were evaluated, often in comparison with other assessment methods for upper limb muscular power and during squat lifts. Reliability and reproducibility were also assessed for repetition and time under tension during leg and bench press exercises. The reproducibility of velocity and power estimates during squat lifts and bench press exercises have been assessed. A critical role has been attributed to participant variability, especially for the lightest loads, with velocity and acceleration variability having a combined negative impact on power reproducibility. Nevertheless, MIMUs can be considered, within the known limits of reproducibility and accuracy, as a tool for monitoring strength–power potentiating complexes, performed with overloads to describe the load–power and load–velocity relationships for specific exercises, to determine the optimal loads for training, to monitor the effects of power training, and to assess the effect of stability conditions on power production.

132In this respect, performance related parameters, obtained using sport-specific capacity tests, or during standardised in-field tests can be of great benefit. However, thus far, few attempts have been made to develop sport-specific capacity tests measuring the athlete’s performance potential in combat sports or to adapt traditional tests, such as vertical jumps, to a specific sport, as done for soccer for analysing vertical jumps with intention to head. Instrumented in-field tests have been developed mainly to assess the performance of wheelchair users, probably because of the higher quality of signal obtainable by attaching the sensors to the wheelchair or athletes’ upper limbs, thus limiting the effect of soft tissue wobbling. These papers testify the potential of MIMUs to assess agility (repeated turn tests), by quantifying shoulder and elbow joint kinematics or wheelchair kinematics, and muscular power (sprint tests) or endurance (distance tests), by quantifying average and peak wheelchair kinematic parameters. A reliable assessment of agility drills in match-like conditions enables prospective research during real matches, as utilised in wheelchair basketball. The description of individual wheelchair mobility can also be used for classification within the sport.

3.4.4. Activity Classification (19 Papers)

133Activity classification papers cover

134the identification of different movement patterns through simple visual inspection (cross-country skiing)

135the identification/evaluation of techniques and styles/tricks, specific movement phases, and events (player tackles and collisions during rugby matches, putt detection and characterization for golf training progress, arm action and ball release performed by cricket fast bowlers during training and competition, eventually used for the detection of illegal events)

136automatic detection and classification of sports activities during wheelchair rugby matches and team sport activities

137automatic classification of training backgrounds and experience levels in runners.

138Activity classification has been performed by measuring body segments in relative or absolute orientation, to detect sprinting, jogging, walking, jumping, cutting, kicking, dribbling, ball strike, tackling, swimming style, trampoline routines, climbing phases, rugby collisions, volleyball actions, tennis strokes, golf putting, and aerial manoeuvres in half-pipe snowboarding and skateboarding.

139Automatic classification (i.e., mapping input data to a predefined category) may be carried out through the following procedure: (i) acquiring an annotated dataset in which, in cases of supervised classification, each datum is associated to a class label; (ii) pre-processing data by filtering, down-sampling or windowing to identify data segments to be classified; (iii) extracting discriminative features, i.e., quantities that hold the information to be used for classification; (iv) training the selected classifier using the training set; (v) using the generated classification rules for prediction of the test set. Activity classification solutions are then often cross-validated, by running several different splits between the training and test sets, to better assess the quality of the methods. Common approaches for this are the N-fold cross validation and the leave-one-subject-out cross-validation method. The selected features/patterns should encode important and intuitive properties of the athlete’s body configuration while remaining constant under global variations. It is desirable that stronger correlations exist between similar features/patterns and poorer correlations between dissimilar features/patterns. They can be simple time/frequency features, such as means, standard deviations, entropy, dominant frequencies, or can be extracted using Discrete Wavelet Transform. Unsupervised approaches for classification cluster the extracted features, often based on k-means analysis, with a predefined cluster number or by selecting the cluster number within a consensus clustering framework. Supervised approaches train the classification system using a set of data with a known classification. The classifiers of this kind used in the revised literature are decision trees (random forest training algorithms, logistic model trees); stepwise discriminant analysis of principal components; k-Nearest Neighbour algorithms (IBk, Lazy IBk); Artificial Neural Networks (RBF Network, MLP network); Support Vector Machines; Naïve Bayesian classifiers; fractals; Hidden Markov or Semi-Markov models; and Hidden Conditional Random Field.

3.5. Device Characteristics

140A systematic analysis of sensors’ positioning and configuration, as implemented in the reviewed studies, is provided in Table 5 and Table 6; sensors attached on the athlete’s body segments are reported in Table 5, while those attached or inserted into sport equipment are depicted in Table 6.

141In regard to the type of inertial sensors used, more than half of the reviewed studies (57%) exploited only 1D/3D accelerometers. In the remaining cases, the measurement of the accelerometer was integrated with other sensors, namely, gyroscopes (25%) or gyroscopes and magnetometers (15%). Only a few studies used 1D/3D gyroscopes in isolation (3%). In regard to the sensors’ features, the ranges of the accelerometers were largely below 10 g (71%) and more than half of the gyroscopes had a range below 1200 deg/s (61%). The most used commercial system was the MinimaxX (considering all the different versions produced by Catapult) followed by Myotest (Myotest), SPI-PRO (GPSports), Xsens systems (Moven suit and MTx sensor), and Physilog (GaitUP). On the other hand, 68% of the reviewed studies implemented custom-made devices and 15% reported no information in this respect.

142With regard to IMU number and position, 67% of the studies exploited a single sensor configuration, 24% used two to five sensors, 7% used more than five sensors, and 2% did not report this information. Typically, the single sensor configuration was inserted into the sport equipment or attached to the trunk–pelvis segment or distal segments, such as the hand and foot. Considering the sports most frequently analysed—swimming and running—the single sensor configuration was preferred in 69% and 60% of the studies, respectively. Several procedures were adopted to attach the sensor onto the athlete’s body segments, from double-side tapes, elastic stretch bands, straps, and Velcro fasteners, to body suit and customized vests (Figure 3). When the sensor was attached to the sport equipment, glue was typically used.

4.1. General Trends

143Coaches, athletes, and support staff are benefitting more and more from the use of magneto-inertial sensor technology. In recent years, a high number of publications have testified that this technology is now suitable for assessing player capacity, technique, and match workload, both for individual athletes and sports teams (Figure 2). Monitoring these aspects can enhance training design and evidence biomechanical fatigue, thus facilitating the development of appropriate injury reduction interventions.

144Technique analysis has been performed for more than 37 sports using MIMUs, to obtain predominantly spatio–temporal parameters, centre of mass kinematics, and body segment orientation, spanning a broad spectrum of objectives. The majority of this literature focuses on describing technique, rather than developing tools to correct technique because exploitation of inertial sensors in sport performance evaluation is rather recent. Only after gaining a deep understanding of MIMUs patterns, for different athletes’ levels, it will be possible to use this tool to revise or to improve the technique of a given athlete. Regarding experimental errors, gyroscopes have demonstrated greater limitations (i.e., drift) than accelerometers, but they are essential when obtaining orientation both as a parameter in dynamic conditions and when removing gravity from acceleration data. As a result, orientation and joint angle estimates can be inaccurate; accuracy depends on task duration, intensity, and soft tissue wobbling, which are dictated by the task and body segment analysed. Drift errors also hinder the accuracy of velocity and position estimates, as obtained through the numerical integration of MIMU signals.

145Combined IMU and GPS data have been used to obtain sensitive and holistic measures of external loading in team sports. Specifically, they enable the detection of abrupt changes in running velocity and direction, as well as impact identification and classification. Data have also been used to quantify the technical demands of team sports and have proved to be sensitive to the effect of factors influencing success. These data can support the manipulation of team sport parameters (e.g., rules, field size, player numbers and position, coach feedback, etc.), which facilitates optimisation of training stimuli.

146MIMU-derived information has proven to be adequate in assisting routine capacity assessment along the vertical direction during explosive performance and maximum dynamic strength tests. This supports the evaluation of learning and progression/regression processes and, therefore, enhances the efficacy of a planned training schedule and the compilation of a training diary. The accuracy and reliability of these assessments are dependent on data collection procedures and equipment. This currently inhibits the development of more complex instrumented in-field tests, occurring in the horizontal plane (e.g., Yo-Yo test, Reactive Agility test).

147The current decade has seen a rapid increase in the availability and use of wearable technology. As sensor utilisation rises across sports and monitoring is allowed during competition (e.g., rugby, baseball, tennis), the challenge becomes providing more intelligent, real-time, accurate information, making it user friendly and offering coaches and athletes actionable insights. In fact, amongst coaches, there is a perceived lack of MIMU related knowledge and known low usage rates, according to a survey of 500 swimming coaches. This aspect can be explained by the fact that most signals obtained from MIMUs are less intuitive than, for example, the position of an anatomical landmark that can be easily investigated and interpreted using standard video analysis. Technology usage is hindered by the expense of adoption and implementation, by the skills required for data collection, extraction, and interpretation (indoor team sports), as well as by the current lack of standardisation procedures. On one hand, technicians and engineers should work on the development of methods aimed at providing coaches with metrics that are informative, concise, and easy to interpret. On the other hand, coaches, athletes, and support staff could enhance their likelihood of use and understanding by partaking in the development process of second generation monitoring systems, by identifying training needs and opportunities. Further, standardisation of relevant procedures is necessary to maximise the technology’s potential.

4.2. Technological Advancements and Future Developments

148Technological advancements may cause significant changes with lasting impacts on the performance and spectatorism of a sport. The most significant recent developments have led to increased commercial sensor affordability and availability of sensor fusion technology and improved power management strategies (for example turning off a gyroscope when not necessary). Sampling frequency and full range scales are now adequate for most sport applications. The technology has reached a stage whereby, for mainstream sports, data collection is easily possible and affordable. For example, new generation gyroscopes allow tracking of fast rotational movements thus enabling the development of in-field training or monitoring devices (tennis), and support tools for referees. The cost of MEMS has been driven down by smartphones which have built-in sensors, including accelerometers, GPS, and often gyroscopes. This enables smartphones to provide information on the motion, environment, position, and orientation of a device to both inexperienced users and experts alike. However, there is limited evidence supporting the use of smartphones over MIMUs. This is potentially due to wearability issues and inaccurate sensors. Wearability has been improved by using smaller sensors which transmit data back to the smartphone; alternatively, MIMUs are being substituted by smart fabrics which use garments as a wearable platform in which to embed sensors and processing units and appear as a potential future alternative/integration to MIMU. To partially overcome the accuracy issue, MIMUs can be complemented by new sensors within a sensor fusion framework; ultra-wideband ranging sensors promise to estimate both orientation and position, whereas barometer sensors estimate only position.

149Until recently, only professional teams and elite individual athletes had access to highly specialised technology. Recreational athletes were restricted to the use of low cost IMUs, such as step counters, whose outcome is often dissatisfactory. However, increasingly more products are being designed for broad participation; often products designed for elite use are later tailored to recreational athletes (examples with costs ranging $70–200 can be found in Lightman). Consequently, it is expected that recreational athletes develop a desire for more detailed and accurate performance data and an increased demand for affordable higher quality intensity/performance trackers, at an intermediate cost (wearables 2.0, potentially including virtual reality). This market demand is driving the democratisation of wearable technology. In the near future, fitness trackers requiring manual and subjective interpretation will be replaced by machine learning tools able to provide real-time monitoring and automatic and objective analysis. Furthermore, this data will provide clear actionable insights, benefitting users of all levels.

150To support athletes in adapting their training and technique with useful insights, feedback systems are required. These systems also reinforce the relationship between the coach’s observations and the athlete’s subjective perception of force and movement. The number and type of sensors is of paramount importance to their efficiency and success. Further, visual feedback can help people with limited experience in MIMU use interpret the signal patterns. Communication technologies allow athletes to document and share their training data, which may enable remote training supervision.

151For these insights to become actionable, coaching systems must integrate sensor measurements with machine learning systems for the purpose of providing intelligent, online feedback. This enables tactical use and workload management, in addition to post game analysis. Physical movement monitoring first requires the definition of a linguistic framework to represent the data symbolically. Real-time or near real-time systems based on robust wireless network solutions can provide quick access to information. Then, the detection of key information and event classification can be performed using models for pattern recognition and classification of the motion or activity performed. Different methods and models, such as Neural Networks, Hidden Markov models, and Support Vector Machines, have proven to be appropriate for this purpose (see Activity classification for further examples). This approach is essential for feedback systems aiming to recognise and evaluate the quality of specific techniques in the field, and eventually during technical–tactical sessions. Machine learning can also automatically provide feedback on how to continue exercising and/or adjust smart sports equipment, depending on environmental conditions. We hypothesise that, using a similar approach, athlete capacity assessment can also be further improved to include measures of sprint ability and agility and by assessing sport-specific tasks covering the entire force–velocity–power spectrum.

4.3. Guidelines and Standards

152There is a call for the standardisation of data collection and analysis procedures amongst several contexts. For team sports, standardisation of physical demands and external load quantification are required, to facilitate comparison between skill levels and within a specific sport. For capacity assessment, standardisation of power measurements is required to further understand the load–power relationship and the training adaptation mechanisms. A list of general key guidelines is here reported that may help in assessing sensor quality, performing sensor calibration and anatomical calibration, properly fixing the sensors and processing the data (Table 7). Nevertheless, complete guidelines and standards for testing should be application- and sport-specific since, for a selected parameter, error is dependent on the task. Additionally, error compensation methods do not necessarily transfer across sports. Therefore, the choice of methodology should be driven by the parameters and the task, with a balance between accuracy and practicability as well as consideration of environmental factors. Parameter-specific recommendations for both sensor type and sensor placement have been developed for running, but more research is required that is specific to other sports and parameters. In open skill sports, for example, the intrinsic variability must be considered in order to obtain reliable performance indicators. Therefore, the sport scientist must have in-depth knowledge of the technology in order to make an informed decision, eventually combining different levels of sensor accuracy according to the aims.

5. Conclusions

153This review demonstrates that magneto- inertial technology, continuously gaining momentum in sports biomechanics, is a reliable tool able to benefit athletes of all levels, especially when complemented within a sensor fusion network. This can ultimately extend and enhance athletes’ careers through improved injury prevention and training specificity. While this reliability is fully exploited in the laboratory, there must be a compromise between the technology’s potential and practicality in the field. When this compromise is not reached, mainly due to the lack of proper standardisation of data acquisition and of tools for the subsequent analysis, the technology may suffer from low usage rates amongst coaches. Better collaboration between the sport biomechanist and practitioner would facilitate overcoming this issue. Future research should focus on ease of use and improved error compensation and analysis procedures able to provide coaches with informative, concise, and easy to interpret metrics. In doing so, MIMUs can facilitate the provision of actionable insights for both the athlete and coach.

References 379

1.
Chambers R., Gabbett T.J., Cole M.H., Beard A. The use of wearable microsensors to quantify sport-specific movements. Sports Med. 2015;45:1065-1081.
Not in our library10.1007/s40279-015-0332-9
2.
Hood S., McBain T., Portas M., Spears I. Measurement in sports biomechanics. Meas. Control. 2012;45:182-186.
Not in our library10.1177/002029401204500604
3.
Li R.T., Kling S.R., Salata M.J., Cupp S.A., Sheehan J., Voos J.E. Wearable performance devices in sports medicine. Sports Health. 2016;8:74-78.
Not in our library10.1177/1941738115616917
4.
Lee J.B., Ohgi Y., James D.A. Sensor fusion: Let’s enhance the performance of performance enhancement. Procedia Eng. 2012;34:795-800.
Not in our library10.1016/j.proeng.2012.04.136
5.
Mendes J.J.A., Vieira M.E.M., Pires M.B., Stevan S.L. Sensor fusion and smart sensor in sports and biomedical applications. Sensors. 2016;16:1569.
Not in our library10.3390/s16101569
6.
Baca A. Innovative diagnostic methods in elite sport. Int. J. Perform. Anal. Sport. 2017;6:148-156.
Not in our library10.1080/24748668.2006.11868380
7.
Knight J.F., Bristow H.W., Anastopoulou S., Baber C., Schwirtz A., Arvanitis T.N. Uses of accelerometer data collected from a wearable system. Pers. Ubiquitous Comput. 2007;11:117-132.
Not in our library10.1007/s00779-006-0070-y
8.
Wagner J.F. About Motion Measurement in Sports Based on Gyroscopes and Accelerometers—An Engineering Point of View. Gyroscopy Navig. 2018;9:1-18.
Not in our library10.1134/s2075108718010091
9.
De Magalhaes F.A., Vannozzi G., Gatta G., Fantozzi S. Wearable inertial sensors in swimming motion analysis: A systematic review. J. Sports Sci. 2015;33:732-745.
Not in our library10.1080/02640414.2014.962574
10.
Callaway A., Cobb J., Jones I. A comparison of video and accelerometer based approaches applied to performance monitoring in swimming. Int. J. Sports Sci. Coach. 2009;4:139-153.
Not in our library10.1260/1747-9541.4.1.139
11.
Higginson B.K. Methods of running gait analysis. Curr. Sports Med. Rep. 2009;8:136-141.
Not in our library10.1249/jsr.0b013e3181a6187a
12.
Norris M., Anderson R., Kenny I.C. Method analysis of accelerometers and gyroscopes in running gait: A systematic review. J. Sports Eng. Technol. 2014;228:3-15.
Not in our library10.1177/1754337113502472
13.
McMaster D.T., Gill N., Cronin J., McGuigan M. A brief review of strength and ballistic assessment methodologies in sport. Sports Med. 2014;44:603-623.
Not in our library10.1007/s40279-014-0145-2
14.
Dugan E.L., Doyle T.L.A., Humphries B., Hasson C.J., Newton R.U. Determining the optimal load for jump squats: A review of methods and calculations. J. Strength Cond. Res. 2004;18:668-674.
Not in our library10.1519/1533-4287(2004)18<668:dtolfj>2.0.co;2
15.
Aughey R.J. Applications of GPS technologies to field sports. Int. J. Sports Physiol. Perform. 2011;6:295-310.
Not in our library10.1123/ijspp.6.3.295
16.
Cummins C., Orr R., O’Connor H., West C. Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Med. 2013;43:1025-1042.
Not in our library10.1007/s40279-013-0069-2
17.
Dellaserra C.L., Gao Y., Ransdell L. Use of integrated technology in team sports: A review of opportunities, challenges, and future directions for athletes. J. Strength Cond. Res. 2014;28:556-573.
Not in our library10.1519/jsc.0b013e3182a952fb
18.
Scott M.T.U., Scott T.J., Kelly V.G. The validity and reliability of global positioning systems in team sport. J. Strength Cond. Res. 2016;30:1470-1490.
Not in our library10.1519/jsc.0000000000001221
19.
Waegli A., Skaloud J. Optimization of two GPS/MEMS-IMU integration strategies with application to sports. GPS Solut. 2009;13:315-326.
Not in our library10.1007/s10291-009-0124-5
20.
World Health Organization. International Classification of Functioning, Disability and Health (ICF). 2001.
Not in our library
21.
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing · 2011
No notice
22.
Luinge H.J., Veltink P.H. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Comput. 2005;43:273-282.
Not in our library10.1007/bf02345966
23.
Sabatini A.M. Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis. Med. Biol. Eng. Comput. 2005;43:94-101.
Not in our library10.1007/bf02345128
24.
Le Sage T., Bindel A., Conway P.P., Justham L.M., Slawson S.E., West A.A. Embedded programming and real-time signal processing of swimming strokes. Sports Eng. 2011;14:1-14.
Not in our library10.1007/s12283-011-0070-7
25.
Hamill J., Caldwell G.E., Derrick T.R. Reconstructing digital signals using Shannon’s sampling theorem. J. Appl. Biomech. 1997;13:226-238.
Not in our library10.1123/jab.13.2.226
26.
Woodman O. An Introduction to Inertial Navigation. 2007.
Not in our library
27.
Camomilla V., Dumas R., Cappozzo A. Human movement analysis: The soft tissue artefact issue. J. Biomech. 2017;62:1-4.
Not in our library10.1016/j.jbiomech.2017.09.001
28.
Liu T., Inoue Y., Shibata K. Measurement of soft tissue deformation to improve the accuracy of a body-mounted motion sensor. J. Med. Devices. 2009;3:35001.
Not in our library10.1115/1.3212558
29.
Lafortune M.A. Three-dimensional acceleration of the tibia during walking and running. J. Biomech. 1991;24:877-886.
Not in our library10.1016/0021-9290(91)90166-k
30.
Cereatti A., Bonci T., Akbarshahi M., Aminian K., Barré A., Begon M., Benoit D.L., Charbonnier C., Dal Maso F., Fantozzi S., et al. Standardization proposal of soft tissue artefact description for data sharing in human motion measurements. J. Biomech. 2017;62:5-13.
Not in our library10.1016/j.jbiomech.2017.02.004
31.
Nokes L., Fairclough J.A., Mintowt-Czyz W.J., Mackie I., Williams J. Vibration analysis of human tibia: The effect of soft tissue on the output from skin-mounted accelerometers. J. Biomed. Eng. 1984;6:223-226.
Not in our library10.1016/0141-5425(84)90107-9
32.
Forner-Cordero A., Mateu-Arce M., Forner-Cordero I., Alcántara E., Moreno J.C., Pons J.L. Study of the motion artefacts of skin-mounted inertial sensors under different attachment conditions. Physiol. Meas. 2008;29:21-31.
Not in our library10.1088/0967-3334/29/4/n01
33.
Decker C., Prasad N., Kawchuk G.N. The reproducibility of signals from skin-mounted accelerometers following removal and replacement. Gait Posture. 2011;34:432-434.
Not in our library10.1016/j.gaitpost.2011.05.024
34.
Bachmann E., Yun X., Peterson C. An investigation of the effects of magnetic variations on inertial/magnetic orientation sensors. Proceedings of the IEEE International Conference on Robotics and Automation. 1115-1122.
Not in our library
35.
Kalman R. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960;82:35-45.
Not in our library10.1115/1.3662552
36.
Madgwick S.O.H., Harrison A.J.L., Vaidyanathan A. Estimation of IMU and MARG orientation using a gradient descent algorithm. Proceedings of the IEEE International Conference on Rehabilitation Robotics. 1-7.
Not in our library10.1109/icorr.2011.5975346
37.
Mahony R., Hamel T., Pflimlin J. Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control. 2008;53:1203-1218.
Not in our library10.1109/tac.2008.923738
38.
Cereatti A., Della Croce U., Sabatini A.M., Müller B., Wolf S.I. Three-dimensional human kinematics estimation using magneto-inertial measurement units. Hand Book of Human Motion. 2017;1-24.
Not in our library
39.
Walker J.A. Estimating velocities and accelerations of animal locomotion: A simulation experiment comparing numerical differentiation algorithms. J. Exp. Biol. 1998;201:981-995.
Not in our library
40.
Yuan Q., Chen I. Localization and velocity tracking of human via 3 IMU sensors. Sens. Actuators A Phys. 2014;212:25-33.
Not in our library10.1016/j.sna.2014.03.004
41.
Myklebust H., Gløersen Ø., Hallén J. Validity of ski skating center-of-mass displacement measured by a single inertial measurement unit. J. Appl. Biomech. 2015;31:492-498.
Not in our library10.1123/jab.2015-0081
42.
Faber G.S., Kingma I., van Dieën J.H. Bottom-up estimation of joint moments during manual lifting using orientation sensors instead of position sensors. J. Biomech. 2010;43:1432-1436.
Not in our library10.1016/j.jbiomech.2010.01.019
43.
Krüger A., McAlpine P., Borrani F., Edelmann-Nusser J. Determination of three-dimensional joint loading within the lower extremities in snowboarding. J. Eng. Med. 2011;226:170-175.
Not in our library10.1177/0954411911426938
44.
Higgins J.P.T., Green S. Cochrane Handbook for Systematic Reviews of Interventions. 2008.
Not in our library
45.
Seshadri D.R., Drummond C., Craker J., Rowbottom J.R., Voos J.E. Wearable devices for sports: New integrated technologies allow coaches, physicians, and trainers to better understand the physical demands of athletes in real time. IEEE Pulse. 2017;8:38-43.
Not in our library10.1109/mpul.2016.2627240
46.
Espinosa H.G., Lee J., James D.A. The inertial sensor: A base platform for wider adoption in sports science applications. J. Fit. Res. 2015;4:13-20.
Not in our library
47.
Armstrong S. Wireless connectivity for health and sports monitoring: A review. Br. J. Sports Med. 2007;41:285-289.
Not in our library10.1136/bjsm.2006.030015
48.
Gabbett T.J. Quantifying the physical demands of collision sports. J. Strength Cond. Res. 2013;27:2319-2322.
Not in our library10.1519/jsc.0b013e318277fd21
49.
Mooney R., Corley G., Godfrey A., Quinlan L., ÓLaighin G. Inertial sensor technology for elite swimming performance analysis: A systematic review. Sensors. 2016;16:18.
Not in our library10.3390/s16010018
50.
Picerno P. Good practice rules for the assessment of the force-velocity relationship in isoinertial resistance exercises. Asian J. Sports Med. 2017;8:e15590.
Not in our library
51.
Dadashi F., Millet G.P., Aminian K. Inertial measurement unit and biomechanial analysis of swimming: An update. Swiss J. Sports Med. Sports Traumatol. 2013;61:28-33.
Not in our library
52.
Ahmadi A., Mitchell E., Richter C., Destelle F., Gowing M., O’Connor N.E., Moran K. Towards automatic activity classification and movement assessment during a sports training session. IEEE Internet Things J. 2015;2:23-32.
Not in our library10.1109/jiot.2014.2377238
53.
Ahmadi A., Rowlands D.D., James D.A. Development of inertial and novel marker-based techniques and analysis for upper arm rotational velocity measurements in tennis. Sports Eng. 2010;12:179-188.
Not in our library10.1007/s12283-010-0044-1
54.
Ahmadi A., Rowlands D., James D.A. Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve. Sports Eng. 2010;2:129-136.
Not in our library10.1080/19346182.2009.9648510
55.
Akins J.S., Heebner N.R., Lovalekar M., Sell T.C. Reliability and validity of instrumented soccer equipment. J. Appl. Biomech. 2015;31:195-201.
Not in our library10.1123/jab.2014-0191
56.
Alexander J.P., Hopkinson T.L., Wundersitz D.W.T., Serpell B.G., Mara J.K., Ball N.B. Validity of a wearable accelerometer device to measure average acceleration values during high-speed running. J. Strength Cond. Res. 2016;30:3007-3013.
Not in our library10.1519/jsc.0000000000001396
57.
Ammann R., Taube W., Wyss T. Accuracy of PARTwear inertial sensor and Optojump optical measurement system for measuring ground contact time during running. J. Strength Cond. Res. 2016;30:2057-2063.
Not in our library10.1519/jsc.0000000000001299
58.
Anderson R., Harrison A., Lyons G.M. Accelerometry-based feedback—Can it improve movement consistency and performance in rowing? Sports Biomech. 2005;4:179-195.
Not in our library10.1080/14763140508522862
59.
Andrew D.P.S., Chow J.W., Knudson D.V., Tillman M.D. Effect of ball size on player reaction and racket acceleration during the tennis volley. J. Sci. Med. Sport. 2003;6:102-112.
Not in our library10.1016/s1440-2440(03)80013-0
60.
Atkinson M., Rosalie S., Netto K. Physical demand of seven closed agility drills. Sports Biomech. 2016;15:473-480.
Not in our library10.1080/14763141.2016.1179781
61.
Auvinet B., Gloria E., Renault G., Barrey E. Runner’s stride analysis: Comparison of kinematic and kinetic analyses under field conditions. Sci. Sports. 2002;17:92-94.
Not in our library10.1016/s0765-1597(02)00122-3
62.
Avvenuti M., Cesarini D., Cimino M.G.C.A. MARS, a multi-agent system for assessing rowers’ coordination via motion-based stigmergy. Sensors. 2013;13:12218-12243.
Not in our library10.3390/s130912218
63.
Bächlin M., Tröster G. Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive Mob. Comput. 2012;8:68-81.
Not in our library10.1016/j.pmcj.2011.05.003
64.
Balsalobre-Fernández C., Kuzdub M., Poveda-Ortiz P., Campo-Vecino J. Del Validity and reliability of the PUSH wearable device to measure movement velocity during the back squat exercise. J. Strength Cond. Res. 2016;30:1968-1974.
Not in our library10.1519/jsc.0000000000001284
65.
Balsalobre-Fernández C., Tejero-Gonzalez C.M., Del Campo-Vecino J., Alonso-Curiel D. The effects of a maximal power training cycle on the strength, maximum power, vertical jump height and acceleration of high-level 400-meter hurdlers. J. Hum. Kinet. 2013;36:119-126.
Not in our library10.2478/hukin-2013-0012
66.
Barrett S., Midgley A.W., Towlson C., Garrett A., Portas M., Lovell R. Within-match PlayerLoad patterns during a simulated soccer match: Potential implications for unit positioning and fatigue management. Int. J. Sports Physiol. Perform. 2016;11:135-140.
Not in our library10.1123/ijspp.2014-0582
67.
Barrett S., Midgley A., Lovell R. PlayerLoad™: Reliability, convergent validity, and influence of unit position during treadmill running. Int. J. Sports Physiol. Perform. 2014;9:945-952.
Not in our library10.1123/ijspp.2013-0418
68.
Beanland E., Main L.C., Aisbett B., Gastin P., Netto K. Validation of GPS and accelerometer technology in swimming. J. Sci. Med. Sport. 2014;17:234-238.
Not in our library10.1016/j.jsams.2013.04.007
69.
Bergamini E., Guillon P., Camomilla V., Pillet H., Skalli W., Cappozzo A. Trunk inclination estimate during the sprint start using an inertial measurement unit: A validation study. J. Appl. Biomech. 2013;29:622-627.
Not in our library10.1123/jab.29.5.622
70.
Bergamini E., Picerno P., Pillet H., Natta F., Thoreux P., Camomilla V. Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit. J. Biomech. 2012;45:1123-1126.
Not in our library10.1016/j.jbiomech.2011.12.020
71.
Bergamini E., Morelli F., Marchetti F., Vannozzi G., Polidori L., Paradisi F., Traballesi M., Cappozzo A., Delussu A.S. Wheelchair propulsion biomechanics in junior basketball players: A method for the evaluation of the efficacy of a specific training program. BioMed Res. Int. 2015;2015:275965.
Not in our library10.1155/2015/275965
72.
Bigelow E.M.R., Elvin N.G., Elvin A.A., Arnoczky S.P. Peak impact accelerations during track and treadmill running. J. Appl. Biomech. 2013;29:639-644.
Not in our library10.1123/jab.29.5.639
73.
Boyd L.J., Ball K., Aughey R.J. Quantifying external load in australian football matches and training using accelerometers. Int. J. Sports Physiol. Perform. 2013;8:44-51.
Not in our library10.1123/ijspp.8.1.44
74.
Boyd L.J., Ball K., Aughey R.J. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int. J. Sports Physiol. Perform. 2011;6:311-321.
Not in our library10.1123/ijspp.6.3.311
75.
Brodie M., Walmsley A., Page W. Fusion motion capture: A prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing. Sports Technol. 2008;1:17-28.
Not in our library10.1080/19346182.2008.9648447
76.
Brown N., Bichler S., Alt W. Detecting repetitions and time features in resistance training using triaxial accelerometry. Sports Technol. 2015;8:35-43.
Not in our library10.1080/19346182.2015.1064935
77.
Buchheit M., Morin J. Assessing stride variables and vertical stiffness with GPS embedded accelerometers preliminary insights for the monitoring of neuromuscular fatigue on the field. J. Sports Sci. Med. 2015;14:698-701.
Not in our library
78.
Buddhadev H.H., Vingren J.L., Duplanty A.A., Hill D.W. Mechanisms underlying the reduced performance measures from using equipment with a counterbalance weight system. J. Strength Cond. Res. 2012;26:641-647.
Not in our library10.1519/jsc.0b013e318245c0b1
79.
Buśko K., Staniak Z., Szark-Eckardt M., Nikolaidis P.T., Mazur-Rózycka J., Łach P., Michalski R., Gajewski J., Górski M. Measuring the force of punches and kicks among combat sport athletes using a modified punching bag with an embedded accelerometer. Acta Bioeng. Biomech. 2016;18:47-54.
Not in our library
80.
Callaway A.J. Measuring kinematic variables in front crawl swimming using accelerometers: A validation study. Sensors. 2015;15:11363-11386.
Not in our library10.3390/s150511363
81.
Callaway A.J., Cobb J.E. Linear acceleration measurement utilizing inter-instrument synchronization: A comparison between accelerometers and motion-based tracking approaches. Meas. Phys. Educ. Exerc. Sci. 2012;16:151-163.
Not in our library10.1080/1091367x.2012.669336
82.
Caruso J.F., Olson N.M., Taylor S.T., McLagan J.R., Shepherd C.M., Borgsmiller J.A., Mason M.L., Riner R.R., Gilliland L., Grisewold S. Front squat data reproducibility collected with a triple-axis accelerometer. J. Strength Cond. Res. 2012;26:40-46.
Not in our library10.1519/jsc.0b013e31821d5ed7
83.
Casartelli N., Müller R., Maffiuletti N.A. Validity and reliability of the Myotest accelerometric system for the assessment of vertical jump height. J. Strength Cond. Res. 2010;24:3186-3193.
Not in our library10.1519/jsc.0b013e3181d8595c
84.
Castagna C., Ganzetti M., Ditroilo M., Giovannelli M., Rocchetti A., Mazi V. Concurrent validity of vertical jump performance assessment systems. J. Strength Cond. Res. 2013;27:761-768.
Not in our library10.1519/jsc.0b013e31825dbcc5
85.
Chakravorti N., Le Sage T., Slawson S.E., Conway P.P., West A.A. Design and implementation of an integrated performance monitoring tool for swimming to extract stroke information at real time. IEEE Trans. Hum.-Mach. Syst. 2013;43:199-213.
Not in our library10.1109/tsmc.2012.2235428
86.
Chandler P.T., Pinder S.J., Curran J.D., Gabbett T.J. Physical demands of training and competition in collegiate netball players. J. Strength Cond. Res. 2014;28:2732-2737.
Not in our library10.1519/jsc.0000000000000486
87.
Chapman R.F., Laymon A.S., Wilhite D.P., McKenzie J.M., Tanner D.A., Stager J.M. Ground contact time as an indicator of metabolic cost in elite distance runners. Med. Sci. Sports Exerc. 2012;44:917-925.
Not in our library10.1249/mss.0b013e3182400520
88.
Chardonnens J., Favre J., Cuendet F., Gremion G., Aminian K. Measurement of the dynamics in ski jumping using a wearable inertial sensor-based system. J. Sports Sci. 2014;32:591-600.
Not in our library10.1080/02640414.2013.845679
89.
Chardonnens J., Favre J., Cuendet F., Gremion G., Aminian K. Characterization of lower-limbs inter-segment coordination during the take-off extension in ski jumping. Hum. Mov. Sci. 2013;32:741-752.
Not in our library10.1016/j.humov.2013.01.010
90.
Chardonnens J., Favre J., Cuendet F., Gremion G., Aminian K. A system to measure the kinematics during the entire ski jump sequence using inertial sensors. J. Biomech. 2013;46:56-62.
Not in our library10.1016/j.jbiomech.2012.10.005
91.
Chardonnens J., Favre J., Le Callennec B., Cuendet F., Gremion G., Aminian K. Automatic measurement of key ski jumping phases and temporal events with a wearable system. J. Sports Sci. 2012;30:53-61.
Not in our library10.1080/02640414.2011.624538
92.
Charlton P.C., Kenneally-Dabrowski C., Sheppard J., Spratford W. A simple method for quantifying jump loads in volleyball athletes. J. Sci. Med. Sport. 2017;20:241-245.
Not in our library10.1016/j.jsams.2016.07.007
93.
RELIABILITY AND VALIDITY OF AN ACCELEROMETRIC SYSTEM FOR ASSESSING VERTICAL JUMPING PERFORMANCE · 2014
Not checked
94.
Chua J.J.C., Fuss F.K., Subic A. Activity identification and classification in wheelchair rugby using fractal dimensions. Sports Eng. 2017;20:1-15.
Not in our library10.1007/s12283-016-0213-y
95.
Cockcroft J., Muller J., Scheffer C. Robust tracking of bicycle crank angles using magneto-inertial sensors, domain constraints and functional frame alignment techniques. J. Sports Eng. Technol. 2016;1-10.
Not in our library10.1177/1754337116652048
96.
Cockcroft J., Muller J.H., Scheffer C. A novel complimentary filter for tracking hip angles during cycling using wireless inertial sensors and dynamic acceleration estimation. IEEE Sens. J. 2014;14:2864-2871.
Not in our library10.1109/jsen.2014.2318897
97.
Comstock B.A., Solomon-Hill G., Flanagan S.D., Earp J.E., Luk H.-Y., Dobbins K.A., Dunn-Lewis C., Fragala M.S., Ho J.-Y., Hatfield D.L., et al. Validity of the Myotest® in measuring force and power production in the squat and bench press. J. Strength Cond. Res. 2011;25:2293-2297.
Not in our library10.1519/jsc.0b013e318200b78c
98.
Cormack S.J., Mooney M.G., Morgan W., McGuigan M.R. Influence of neuromuscular fatigue on accelerometer load in elite Australian football players. Int. J. Sports Physiol. Perform. 2013;8:373-378.
Not in our library10.1123/ijspp.8.4.373
99.
Cormack S.J., Smith R.L., Mooney M.M., Young W.B., O’Brien B.J. Accelerometer load as a measure of activity profile in different standards of netball match play. Int. J. Sports Physiol. Perform. 2014;9:283-291.
Not in our library10.1123/ijspp.2012-0216
100.
Crewther B.T., Cunningham D.J., Cook C., Owen N. Validating two systems for estimating force and power. Int. J. Sports Med. 2011;32:254-258.
Not in our library10.1055/s-0030-1270487
101.
Cummins C., Orr R. Analysis of physical collisions in elite national rugby league match play. Int. J. Sports Physiol. Perform. 2015;10:732-739.
Not in our library10.1123/ijspp.2014-0541
102.
Cunniffe B., Proctor W., Baker J.S., Davies B. An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. J. Strength Cond. Res. 2009;23:1195-1203.
Not in our library10.1519/jsc.0b013e3181a3928b
103.
Da Silva B.V.C., de Moura Simim M.A., Marocolo M., Franchini E., da Mota G.R. Optimal load for the peak power and maximal strength of the upper body in Brazilian Jiu-Jitsu athletes. J. Strength Cond. Res. 2015;29:1616-1621.
Not in our library10.1519/jsc.0000000000000799
104.
Dadashi F., Millet G.P., Aminian K. A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU. Pervasive Mob. Comput. 2015;19:37-46.
Not in our library10.1016/j.pmcj.2014.03.001
105.
Dadashi F., Millet G.P., Aminian K. Gaussian process framework for pervasive estimation of swimming velocity with body-worn IMU. Electron. Lett. 2013;49:44-45.
Not in our library10.1049/el.2012.3684
106.
Dadashi F., Crettenand F., Millet G.P., Aminian K. Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit. Sensors. 2012;12:12927-12939.
Not in our library10.3390/s121012927
107.
Dadashi F., Crettenand F., Millet G.P., Seifert L., Komar J., Aminian K. Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals. J. Sports Sci. 2013;31:1251-1260.
Not in our library10.1080/02640414.2013.778420
108.
Dadashi F., Millet G.P., Aminian K. Front-crawl stroke descriptors variability assessment for skill characterization. J. Sports Sci. 2016;34:1405-1412.
Not in our library10.1080/02640414.2015.1114134
109.
Dalen T., Ingebrigtsen J., Ettema G., Hjelde G.H., Wisløff U. Player load, acceleration, and deceleration during forty-five competitive matches of elite soccer. J. Strength Cond. Res. 2016;30:351-359.
Not in our library10.1519/jsc.0000000000001063
110.
Davey N., Anderson M., James D.A. Validation trial of an accelerometer-based sensor platform for swimming. Sports Technol. 2008;1:202-207.
Not in our library10.1080/19346182.2008.9648474
111.
Davies M.J., Young W., Farrow D., Bahnert A. Comparison of small-sided games on agility demands in elite australian football. Int. J. Sports Physiol. Perform. 2013;8:139-147.
Not in our library10.1123/ijspp.8.2.139
112.
Del Coso J., Portillo J., Salinero J.J., Lara B., Abian-Vicen J., Areces F. Caffeinated energy drinks improve high-speed running in elite field hockey players. Int. J. Sport Nutr. Exerc. Metab. 2016;26:26-32.
Not in our library10.1123/ijsnem.2015-0128
113.
Dieu O., Vanhelst J., Bui-Xuân G., Blondeau T., Fardy P.S., Mikulovic J. Relationship between tactics and energy expenditure according to level of experience in badminton. Percept. Mot. Skills. 2014;119:455-467.
Not in our library10.2466/29.pms.119c21z3
114.
Dovgalecs V., Boulanger J., Seifert L., Orth D., Hérault R., Coeurjolly J.F., Davids K. Movement phase detection in climbing. Sports Technol. 2014;7:174-182.
Not in our library10.1080/19346182.2015.1064128
115.
Dowling A.V., Favre J., Andriacchi T.P. A wearable system to assess risk for anterior cruciate ligament injury during jump landing: Measurements of temporal events, jump height, and sagittal plane kinematics. J. Biomech. Eng. 2011;133:071008.
Not in our library10.1115/1.4004413
116.
Dubus G. Evaluation of four models for the sonification of elite rowing. J. Multimodal User Interfaces. 2012;5:143-156.
Not in our library10.1007/s12193-011-0085-1
117.
Elvin N.G., Elvin A.A., Arnoczky S.P. Correlation between ground reaction force and tibial acceleration in vertical jumping. J. Appl. Biomech. 2007;23:180-189.
Not in our library10.1123/jab.23.3.180
118.
Enders H., von Tscharner V., Nigg B.M. The effects of preferred and non-preferred running strike patterns on tissue vibration properties. J. Sci. Med. Sport. 2014;17:218-222.
Not in our library10.1016/j.jsams.2013.03.015
119.
Fantozzi S., Giovanardi A., de Magalhaes F.A., Di Michele R., Cortesi M., Gatta G. Assessment of three-dimensional joint kinematics of the upper limb during simulated swimming using wearable inertial-magnetic measurement units. J. Sports Sci. 2016;34:1073-1080.
Not in our library10.1080/02640414.2015.1088659
120.
Fasel B., Favre J., Chardonnens J., Gremion G., Aminian K. An inertial sensor-based system for spatio-temporal analysis in classic cross-country skiing diagonal technique. J. Biomech. 2015;48:3199-3205.
Not in our library10.1016/j.jbiomech.2015.07.001
121.
Fasel B., Praz C., Kayser B., Aminian K. Measuring spatio-temporal parameters of uphill ski-mountaineering with ski-fixed inertial sensors. J. Biomech. 2016;49:3052-3055.
Not in our library10.1016/j.jbiomech.2016.06.017
122.
Fasel B., Spörri J., Gilgien M., Boffi G., Chardonnens J., Müller E., Aminian K. Three-dimensional body and centre of mass kinematics in alpine ski racing using differential GNSS and inertial sensors. Remote Sens. 2016;8:671.
Not in our library10.3390/rs8080671
123.
Flores F.J., Sedano S., de Benito A.M., Redondo J.C. Validity and reliability of a 3-axis accelerometer for measuring weightlifting movements. Int. J. Sports Sci. Coach. 2016;11:872-879.
Not in our library10.1177/1747954116676114
124.
Fulton S.K., Pyne D.B., Burkett B. Quantifying freestyle kick-count and kick-rate patterns in Paralympic swimming. J. Sports Sci. 2009;27:1455-1461.
Not in our library10.1080/02640410903062936
125.
Fulton S.K., Pyne D.B., Burkett B. Optimizing kick rate and amplitude for Paralympic swimmers via net force measures. J. Sports Sci. 2011;29:381-387.
Not in our library10.1080/02640414.2010.536247
126.
Fulton S.K., Pyne D.B., Burkett B. Validity and reliability of kick count and rate in freestyle using inertial sensor technology. J. Sports Sci. 2009;27:1051-1058.
Not in our library10.1080/02640410902998247
127.
Gabbett T., Gahan C. Repeated high-intensity effort activity in relation to tries scored and conceded during rugby league match-play. Int. J. Sports Physiol. Perform. 2016;11:530-534.
Not in our library10.1123/ijspp.2015-0266
128.
Gabbett T.J. Relationship between accelerometer load, collisions, and repeated high-intensity effort activity in rugby league players. J. Strength Cond. Res. 2015;29:3424-3431.
Not in our library10.1519/jsc.0000000000001017
129.
Gabbett T.J. Influence of the opposing team on the physical demands of elite rugby league match play. J. Strength Cond. Res. 2013;27:1629-1635.
Not in our library10.1519/jsc.0b013e318274f30e
130.
Gabbett T.J. Sprinting patterns of national rugby league competition. J. Strength Cond. Res. 2012;26:121-130.
Not in our library10.1519/jsc.0b013e31821e4c60
131.
Gabbett T.J. Influence of playing standard on the physical demands of junior rugby league tournament match-play. J. Sci. Med. Sport. 2014;17:212-217.
Not in our library10.1016/j.jsams.2013.03.013
132.
Gabbett T.J., Jenkins D.G., Abernethy B. Influence of wrestling on the physiological and skill demands of small-sided games. J. Strength Cond. Res. 2012;26:113-120.
Not in our library10.1519/jsc.0b013e31821d97f4
133.
Gabbett T.J., Jenkins D.G., Abernethy B. Physical collisions and injury in professional rugby league match-play. J. Sci. Med. Sport. 2011;14:210-215.
Not in our library10.1016/j.jsams.2011.01.002
134.
Gabbett T.J., Jenkins D.G., Abernethy B. Physical demands of professional rugby league training and competition using microtechnology. J. Sci. Med. Sport. 2012;15:80-86.
Not in our library10.1016/j.jsams.2011.07.004
135.
Gabbett T.J., Abernethy B., Jenkins D.G. Influence of field size on the physiological and skill demands of small-sided games in junior and senior rugby league players. J. Strength Cond. Res. 2012;26:487-491.
Not in our library10.1519/jsc.0b013e318225a371
136.
Gageler W.H., Wearing S., James D.A., Grove K. Automatic jump detection method for athlete monitoring and performance in volleyball. Int. J. Perform. Anal. Sport. 2015;15:284-296.
Not in our library10.1080/24748668.2015.11868793
137.
Gallo T., Cormack S., Gabbett T., Williams M., Lorenzen C. Characteristics impacting on session rating of perceived exertion training load in Australian footballers. J. Sports Sci. 2015;33:467-475.
Not in our library10.1080/02640414.2014.947311
138.
Gandy E.A., Bondi A., Hogg R., Pigott T.M.C. A preliminary investigation of the use of inertial sensing technology for the measurement of hip rotation asymmetry in horse riders. Sports Technol. 2014;7:79-88.
Not in our library10.1080/19346182.2014.905949
139.
Gao Z., Song B., Liu M., Song G., Sun W., Ge Y. Design and application of a multidimensional acceleration sensor for coaching of shot-put athletes. Sens. Actuators A Phys. 2009;149:213-220.
Not in our library10.1016/j.sna.2008.11.029
140.
García-Pérez J.A., Pérez-Soriano P., Llana Belloch S., Lucas-Cuevas A.G., Sánchez-Zuriaga D., Lucas-Cuevas Á.G., Sánchez-Zuriaga D. Effects of treadmill running and fatigue on impact acceleration in distance running. Sports Biomech. 2014;13:259-266.
Not in our library10.1080/14763141.2014.909527
141.
Gašic T., Bubanj S., Živković M., Stanković R., Bubanj R., Obradović B. Difference in the explosive strength of upper extremities between athletes in relation to their sport activity, type of engagement in sport and gender. Sport Sci. 2011;4:63-67.
Not in our library
142.
Gastin P.B., Mclean O.C., Breed R.V.P., Spittle M. Tackle and impact detection in elite Australian football using wearable microsensor technology. J. Sports Sci. 2014;32:947-953.
Not in our library10.1080/02640414.2013.868920
143.
Gastin P.B., McLean O., Spittle M., Breed R.V.P. Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J. Sci. Med. Sport. 2013;16:589-593.
Not in our library10.1016/j.jsams.2013.01.007
144.
Gaudino P., Iaia F.M., Strudwick A.J., Hawkins R.D., Alberti G., Atkinson G., Gregson W. Factors influencing perception of effort (session-RPE) during elite soccer training. Int. J. Sports Physiol. Perform. 2015;10:860-864.
Not in our library10.1123/ijspp.2014-0518
145.
Gescheit D.T., Cormack S.J., Reid M., Duffield R. Consecutive days of prolonged tennis match play: Performance, physical, and perceptual responses in trained players. Int. J. Sports Physiol. Perform. 2015;10:913-920.
Not in our library10.1123/ijspp.2014-0329
146.
Ghasemzadeh H., Jafari R. Coordination analysis of human movements with body sensor networks: A signal processing model to evaluate baseball swings. IEEE Sens. J. 2011;11:603-610.
Not in our library10.1109/jsen.2010.2048205
147.
Ghasemzadeh H., Loseu V., Jafari R. Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf. J. Ambient Intell. Smart Environ. 2009;1:173-184.
Not in our library
148.
Giandolini M., Poupard T., Gimenez P., Horvais N., Millet G.Y., Morin J.B., Samozino P. A simple field method to identify foot strike pattern during running. J. Biomech. 2014;47:1588-1593.
Not in our library10.1016/j.jbiomech.2014.03.002
149.
Gindre C., Lussiana T., Hebert-Losier K., Morin J.B. Reliability and validity of the Myotest® for measuring running stride kinematics. J. Sports Sci. 2016;34:664-670.
Not in our library10.1080/02640414.2015.1068436
150.
Giroux C., Rabita G., Chollet D., Guilhem G. What is the best method for assessing lower limb force-velocity relationship? Int. J. Sports Med. 2014;36:143-149.
Not in our library10.1055/s-0034-1385886
151.
Gomez-Piriz P.T., Sanchez E.T., Manrique D.C., Gonzalez E.P. Reliability and comparability of the accelerometer and the linear position measuring device in resistance training. J. Strength Cond. Res. 2012;27:1664-1670.
Not in our library10.1519/jsc.0b013e318269f809
152.
Gonçalves B.V., Figueira B.E., Maçãs V., Sampaio J. Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. J. Sports Sci. 2014;32:191-199.
Not in our library10.1080/02640414.2013.816761
153.
Gouttebarge V., Wolfard R., Griek N., De Ruiter C.J., Boschman J.S., VanDieën J.H. Reproducibility and validity of the myotest for measuring step frequency and ground contact time in recreational runners. J. Hum. Kinet. 2015;45:19-26.
Not in our library10.1515/hukin-2015-0003
154.
Groh B.H., Flaschka J., Wirth M., Kautz T., Fleckenstein M., Eskofier B.M. Wearable real-time skateboard trick visualization and its community perception. IEEE Comput. Graph. Appl. 2016;36:12-18.
Not in our library10.1109/mcg.2016.95
155.
Gullstrand L., Halvorsen K., Tinmark F., Eriksson M., Nilsson J. Measurements of vertical displacement in running, a methodological comparison. Gait Posture. 2009;30:71-75.
Not in our library10.1016/j.gaitpost.2009.03.001
156.
Hagem R.M., O’Keefe S.G., Fickenscher T., Thiel D.V. Self contained adaptable optical wireless communications system for stroke rate during swimming. IEEE Sens. J. 2013;13:3144-3151.
Not in our library10.1109/jsen.2013.2262933
157.
Hamill J., Derrick T.R., Holt K.G. Shock attenuation and stride frequency during running. Hum. Mov. Sci. 1995;14:45-60.
Not in our library10.1016/0167-9457(95)00004-c
158.
Hang Y.S., Lippert F.G., Spolek G.A., Frankel V.H., Harrington R.M. Biomechanical study of the pitching elbow. Int. Orthop. 1979;3:217-223.
Not in our library10.1007/bf00265715
159.
Harding J.W., Mackintosh C.G., Martin D.T., Hahn A.G., James D.A. Automated scoring for elite half-pipe snowboard competition: Important sporting development or techno distraction? Sports Technol. 2009;1:277-290.
Not in our library10.1080/19346182.2008.9648485
160.
Hausswirth C., Le Meur Y., Couturier A., Bernard T., Brisswalter J. Accuracy and repeatability of the Polar® RS800sd to evaluate stride rate and running speed. Int. J. Sports Med. 2009;30:354-359.
Not in our library10.1055/s-0028-1105936
161.
Helten T., Brock H., Muller M., Seidel H.P. Classification of trampoline jumps using inertial sensors. Sports Eng. 2011;14:155-164.
Not in our library10.1007/s12283-011-0081-4
162.
Herren R., Sparti A., Aminian K., Schutz Y. The prediction of speed and incline in outdoor running in humans using accelerometry. Med. Sci. Sports Exerc. 1999;31:1053-1059.
Not in our library10.1097/00005768-199907000-00020
163.
Hoppe M., Baumgart C., Freiwald J. Do running activities of adolescent and adult tennis players differ during play? Int. J. Sport Nutr. Exerc. Metab. 2011;11:793-801.
Not in our library10.1123/ijspp.2015-0141
164.
Hou P. The study on swimming exercises based on 3D accelerometer data analysis. Int. J. Adv. Comput. Technol. 2012;4:239-245.
Not in our library
165.
Hurst H.T., Atkins S., Kirk C. Reliability of a portable accelerometer for measuring workload during mixed martial arts. J. Athl. Enhanc. 2014;3:5.
Not in our library
166.
Innocenti B., Facchielli D., Torti S., Verza A. Analysis of biomechanical quantitites during a squat jump: Evaluation of a performance index. J. Strength Cond. Res. 2006;20:709-715.
Not in our library10.1519/r-17815.1
167.
Jaitner T., Schmidt M., Nolte K., Rheinlander C., Wille S., Wehn N. Vertical jump diagnosis for multiple athletes using a wearable inertial sensor unit. Sports Technol. 2015;8:51-57.
Not in our library10.1080/19346182.2015.1117476
168.
James D.A., Leadbetter R.I., Neeli M.R., Burkett B.J., Thiel D.V., Lee J.B. An integrated swimming monitoring system for the biomechanical analysis of swimming strokes. Sports Technol. 2011;4:141-150.
Not in our library10.1080/19346182.2012.725410
169.
Janssen I., Sachlikidis A. Validity and reliability of intra-stroke kayak velocity and acceleration using a GPS-based accelerometer. Sports Biomech. 2010;9:47-56.
Not in our library10.1080/14763141003690229
170.
Jarning J.M., Mok K.M., Hansen B.H., Bahr R. Application of a tri-axial accelerometer to estimate jump frequency in volleyball. Sports Biomech. 2015;14:95-105.
Not in our library10.1080/14763141.2015.1027950
171.
Jensen U., Schmidt M., Hennig M., Dassler F.A., Jaitner T., Eskofier B.M. An IMU-based mobile system for golf putt analysis. Sports Eng. 2015;18:123-133.
Not in our library10.1007/s12283-015-0171-9
172.
Jidovtseff B., Croisier J., Lhermerout C. The concept of iso-inertial assessment: Reproducibility analysis and descriptive data. Isokinet. Exerc. Sci. 2006;14:53-62.
Not in our library
173.
Jones I.C., Miller D.I. Influence of fulcrum position on springboard response and takeoff performance in the running approach. J. Appl. Biomech. 1996;12:383-408.
Not in our library10.1123/jab.12.3.383
174.
Kawabata M., Goto K., Fukusaki C., Sasaki K., Hihara E., Mizushina T., Ishii N. Acceleration patterns in the lower and upper trunk during running. J. Sports Sci. 2013;31:1841-1853.
Not in our library10.1080/02640414.2013.805884
175.
Keaney E., Withers S., Parker-Simmons S., Gastin P., Netto K. The training load of aerial skiing. Int. J. Perform. Anal. Sport. 2016;16:726-736.
Not in our library10.1080/24748668.2016.11868919
176.
Kelly D., Coughlan G.F., Green B.S., Caulfield B. Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Eng. 2012;15:81-92.
Not in our library10.1007/s12283-012-0088-5
177.
Kelly P., Conaire C.O., O’Connor N.E., Hodgins J. Motion synthesis for sports using unobtrusive lightweight body-worn and environment sensing. Comput. Graph. Forum. 2013;32:48-60.
Not in our library10.1111/cgf.12143
178.
Kempton T., Sullivan C., Bilsborough J.C., Cordy J., Coutts A.J. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J. Sci. Med. Sport. 2015;18:109-113.
Not in our library10.1016/j.jsams.2013.12.006
179.
King K., Hough J., McGinnis R., Perkins N.C. A new technology for resolving the dynamics of a swinging bat. Sports Eng. 2012;15:41-52.
Not in our library10.1007/s12283-012-0084-9
180.
King K., Perkins N.C., Churchill H., McGinnis R., Doss R., Hickland R. Bowling ball dynamics revealed by miniature wireless MEMS inertial measurement unit. Sports Eng. 2011;13:95-104.
Not in our library10.1007/s12283-010-0054-z
181.
King K., Yoon S.W., Perkins N.C., Najafi K. Wireless MEMS inertial sensor system for golf swing dynamics. Sens. Actuators A Phys. 2008;141:619-630.
Not in our library10.1016/j.sna.2007.08.028
182.
Kirk C., Hurst H.T., Atkins S. Measuring the workload of mixed martial arts using accelerometry, time motion analysis and lactate. Int. J. Perform. Anal. Sport. 2015;15:359-370.
Not in our library10.1080/24748668.2015.11868798
183.
Kirk C., Hurst H., Atkins S. Comparison of the training loads of mixed martial arts techniques in isolated training and open sparring. J. Combat Sports Martial Arts. 2015;6:15-20.
Not in our library10.5604/20815735.1174226
184.
Kobsar D., Osis S.T., Hettinga B.A., Ferber R. Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners. J. Biomech. 2014;47:2508-2511.
Not in our library10.1016/j.jbiomech.2014.04.017
185.
Koda H., Sagawa K., Kuroshima K., Tsukamoto T., Urita K., Ishibashi Y. 3D measurement of forearm and upper arm during throwing motion using body mounted sensor. J. Adv. Mech. Des. Syst. Manuf. 2010;4:167-178.
Not in our library10.1299/jamdsm.4.167
186.
Koshida S., Urabe Y., Miyashita K., Iwai K., Kagimori A. Muscular outputs during dynamic bench press under stable versus unstable conditions. J. Strength Cond. Res. 2008;22:1584-1588.
Not in our library10.1519/jsc.0b013e31817b03a1
187.
Križaj D., Čuk I. Can miniature accelerometers attached to the gymnastics springboard be used for take-off analysis? Sci. Gymnast. J. 2015;7:69-80.
Not in our library
188.
Krüger A., Edelmann-Nusser J. Biomechanical analysis in freestyle snowboarding: Application of a full-body inertial measurement system and a bilateral insole measurement system. Sports Technol. 2009;2:17-23.
Not in our library10.1080/19346182.2009.9648494
189.
Krüger A., Edelmann-Nusser J. Application of a full body inertial measurement system in alpine skiing: A comparison with an optical video based system. J. Appl. Biomech. 2010;26:516-521.
Not in our library10.1123/jab.26.4.516
190.
Laffaye G., Collin J.M., Levernier G., Padulo J. Upper-limb power test in rock-climbing. Int. J. Sports Med. 2014;35:670-675.
Not in our library10.1055/s-0033-1358473
191.
Le Bris R., Billat V., Auvinet B., Chaleil D., Hamard L., Barrey E. Effect of fatigue on stride pattern continuously measured by an accelerometric gait recorder in middle distance runners. J. Sports Med. Phys. Fit. 2006;46:227-231.
Not in our library
192.
Le Sage T., Conway P., Cossor J., Slawson S., West A. A wireless sensor system for monitoring the performance of a swimmer’s tumble turn. J. Sports Eng. Technol. 2013;227:161-171.
Not in our library10.1177/1754337112467881
193.
Le Sage T., Conway P., Slawson S., West A. Development of a wireless sensor network for use as an automated system for monitoring swimming starts. J. Sports Eng. Technol. 2013;227:184-195.
Not in our library10.1177/1754337112462945
194.
Lee J.B., Mellifont R.B., Burkett B.J. The use of a single inertial sensor to identify stride, step, and stance durations of running gait. J. Sci. Med. Sport. 2010;13:270-273.
Not in our library10.1016/j.jsams.2009.01.005
195.
Lee J.B., Mellifont R.B., Burkett B.J., James D.A. Detection of illegal race walking: A tool to assist coaching and judging. Sensors. 2013;13:16065-16074.
Not in our library10.3390/s131216065
196.
Lee J.B., Sutter K.J., Askew C.D., Burkett B.J. Identifying symmetry in running gait using a single inertial sensor. J. Sci. Med. Sport. 2010;13:559-563.
Not in our library10.1016/j.jsams.2009.08.004
197.
Lee J., Leadbetter R., Ohgi Y., Thiel D., Burkett B., James D.A. Quantifying and assessing biomechanical differences in swim turn using wearable sensors. Sports Technol. 2011;4:128-133.
Not in our library10.1080/19346182.2012.725171
198.
Leite M.A., Sasaki J.E., Lourenço C.L.M., Zanetti H.R., Cruz L.G., da Mota G.R., Mendes E.L. Medicine ball throw test predicts arm power in rugby sevens players. Rev. Bras. Cineantropometria Desempenho Hum. 2016;18:166-176.
Not in our library10.5007/1980-0037.2016v18n2p166
199.
Lesinski M., Muehlbauer T., Granacher U. Concurrent validity of the Gyko inertial sensor system for the assessment of vertical jump height in female sub-elite youth soccer players. BMC Sports Sci. Med. Rehabil. 2016;8:35.
Not in our library10.1186/s13102-016-0061-x
200.
Lin S.P., Sung W.H., Kuo T.B.J., Chen J.J. Development and application of gravity acceleration measurement in running kinematic analysis. ICIC Express Lett. 2015;9:1349-1354.
Not in our library
201.
Lin S.-P., Sung W.-H., Kuo F.-C., Kuo T.B.J., Chen J.-J. Impact of center-of-mass acceleration on the performance of ultramarathon runners. J. Hum. Kinet. 2014;44:41-52.
Not in our library10.2478/hukin-2014-0109
202.
Lindsay T.R., Noakes T.D., McGregor S.J. Effect of treadmill versus overground running on the structure of variability of stride timing. Percept. Mot. Skills. 2014;118:331-346.
Not in our library10.2466/30.26.pms.118k18w8
203.
Lindsay T.R., Yaggie J.A., McGregor S.J. A wireless accelerometer node for reliable and valid measurement of lumbar accelerations during treadmill running. Sports Biomech. 2016;15:11-22.
Not in our library10.1080/14763141.2015.1123760
204.
Little C., Lee J.B.J.J.B., James D.A., Davison K. An evaluation of inertial sensor technology in the discrimination of human gait. J. Sports Sci. 2013;31:1312-1318.
Not in our library10.1080/02640414.2013.779739
205.
Llosa J., Vilajosana I., Vilajosana X., Navarro N., Suriach E., Marqus J.M. REMOTE, a wireless sensor network based system to monitor rowing performance. Sensors. 2009;9:7069-7082.
Not in our library10.3390/s90907069
206.
Lomond K.V., Turcotte R.A., Pearsall D.J. Three-dimensional analysis of blade contact in an ice hockey slap shot, in relation to player skill. Sports Eng. 2007;10:87-100.
Not in our library10.1007/bf02844206
207.
Losnegard T., Myklebust H., Skattebo O., Stadheim H.K., Sandbakk Ø., Hallén J. The influence of pole length on performance, O2 cost, and kinematics in double poling. Int. J. Sports Physiol. Perform. 2017;12:211-217.
Not in our library10.1123/ijspp.2015-0754
208.
Lovell T.W.J., Sirotic A.C., Impellizzeri F.M., Coutts A.J. Factors affecting perception of effort (session rating of perceived exertion) during rugby league training. Int. J. Sports Physiol. Perform. 2013;8:62-69.
Not in our library10.1123/ijspp.8.1.62
209.
Lowery R.P., Joy J.M., Brown L.E., Oliveira de Souza E., Wistocki D.R., Davis G.S., Naimo M.A., Zito G.A., Wilson J.M. Effects of static stretching on 1-mile uphill run performance. J. Strength Cond. Res. 2014;28:161-167.
Not in our library10.1519/jsc.0b013e3182956461
210.
Luteberget L.S., Spencer M. High-intensity events in international women’s team handball matches. Int. J. Sports Physiol. Perform. 2017;12:56-61.
Not in our library10.1123/ijspp.2015-0641
211.
Macdermid P.W., Fink P.W., Stannard S.R. Transference of 3D accelerations during cross country mountain biking. J. Biomech. 2014;47:1829-1837.
Not in our library10.1016/j.jbiomech.2014.03.024
212.
Macdermid P.W., Fink P.W., Stannard S.R. The effects of vibrations experienced during road vs off-road cycling. Int. J. Sports Med. 2015;36:783-788.
Not in our library10.1055/s-0034-1398534
213.
Magnúsdóttir Á., Þorgilsson B., Karlsson B. Comparing three devices for jump height measurement in a heterogeneous group of subjects. J. Strength Cond. Res. 2014;28:2837-2844.
Not in our library10.1519/jsc.0000000000000464
214.
Marin-Perianu R., Marin-Perianu M., Havinga P., Taylor S., Begg R., Palaniswami M., Rouffet D. A performance analysis of a wireless body-area network monitoring system for professional cycling. Pers. Ubiquitous Comput. 2013;17:197-209.
Not in our library10.1007/s00779-011-0486-x
215.
Marsland F., Lyons K., Anson J., Waddington G., Macintosh C., Chapman D. Identification of cross-country skiing movement patterns using micro-sensors. Sensors. 2012;12:5047-5066.
Not in our library10.3390/s120405047
216.
Mason B.S., Rhodes J.M., Goosey-Tolfrey V.L. Validity and reliability of an inertial sensor for wheelchair court sports performance. J. Appl. Biomech. 2014;30:326-331.
Not in our library10.1123/jab.2013-0148
217.
Mattes K., Schaffert N. New measuring and on water coaching device for rowing. J. Hum. Sport Exerc. 2010;5:226-239.
Not in our library10.4100/jhse.2010.52.11
218.
Mauch M., Rennbahn P.A., nat Hans-Joachim Rist R., Xaver Kaelin M. Reliability and validity of two measurement systems in the quantification of jump performance. Schweiz. Z. Sportmed. Sporttraumatol. 2014;62:57-63.
Not in our library
219.
McCurdy K.W., Walker J.L., Langford G.A., Kutz M.R., Guerrero J.M., McMillan J. The relationship between kinematic determinants of jump and sprint performance in division I women soccer players. J. Strength Cond. Res. 2010;24:3200-3208.
Not in our library10.1519/jsc.0b013e3181fb3f94
220.
McGinnis R.S., Cain S.M., Davidson S.P., Vitali R.V., Perkins N.C., McLean S.G. Quantifying the effects of load carriage and fatigue under load on sacral kinematics during countermovement vertical jump with IMU-based method. Sports Eng. 2016;19:21-34.
Not in our library10.1007/s12283-015-0185-3
221.
McGinnis R.S., Perkins N.C. A highly miniaturized, wireless inertial measurement unit for characterizing the dynamics of pitched baseballs and softballs. Sensors. 2012;12:11933-11945.
Not in our library10.3390/s120911933
222.
McGrath D., Greene B.R., O’Donovan K.J., Caulfield B. Gyroscope-based assessment of temporal gait parameters during treadmill walking and running. Sports Eng. 2012;15:207-213.
Not in our library10.1007/s12283-012-0093-8
223.
McGregor S.J., Busa M.A., Skufca J., Yaggie J.A., Bollt E.M. Control entropy identifies differential changes in complexity of walking and running gait patterns with increasing speed in highly trained runners. Chaos. 2009;19:26109.
Not in our library10.1063/1.3147423
224.
McGregor S.J., Busa M.A., Yagie J.A., Bollt E.M. High resolution MEMS accelerometers to estimate VO2 and compare running mechanics between highly trained inter-collegiate and untrained runners. PLoS ONE. 2009;4:e7355.
Not in our library10.1371/journal.pone.0007355
225.
McLellan C.P., Lovell D.I. Neuromuscular responses to impact and collision during elite rugby league match play. J. Strength Cond. Res. 2012;26:1431-1440.
Not in our library10.1519/jsc.0b013e318231a627
226.
McLellan C.P., Lovell D.I., Gass G.C. Performance analysis of elite rugby league match play using global positioning systems. J. Strength Cond. Res. 2011;25:1703-1710.
Not in our library10.1519/jsc.0b013e3181ddf678
227.
McNamara D.J., Gabbett T.J., Chapman P., Naughton G., Farhart P. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int. J. Sports Physiol. Perform. 2015;10:71-75.
Not in our library10.1123/ijspp.2014-0062
228.
Meamarbashi A., Hossaini S.R.A. Application of novel inertial technique to compare the kinematics and kinetics of the legs in the soccer instep kick. J. Hum. Kinet. 2010;23:5-13.
Not in our library10.2478/v10078-010-0001-x
229.
Mercer J.A., Devita P., Derrick T.R., Bates B.T. Individual effects of stride length and frequency on shock attenuation during running. Med. Sci. Sports Exerc. 2003;35:307-313.
Not in our library10.1249/01.mss.0000048837.81430.e7
230.
Mercer J.A., Vance J., Hreljac A., Hamill J. Relationship between shock attenuation and stride length during running at different velocities. Eur. J. Appl. Physiol. 2002;87:403-408.
Not in our library10.1007/s00421-002-0646-9
231.
Michahelles F., Schiele B. Sensing and monitoring professional skiers. IEEE Pervasive Comput. 2005;4:40-46.
Not in our library10.1109/mprv.2005.66
232.
Mitchell E., Monaghan D., O’Connor N.E. Classification of sporting activities using smartphone accelerometers. Sensors. 2013;13:5317-5337.
Not in our library10.3390/s130405317
233.
Monnet T., Decatoire A., Lacouture P. Comparison of algorithms to determine jump height and flight time from body mounted accelerometers. Sports Eng. 2014;17:249-259.
Not in our library10.1007/s12283-014-0155-1
234.
Montgomery P.G., Pyne D.B., Minahan C.L. The physical and physiological demands of basketball training and competition. Int. J. Sports Physiol. Perform. 2010;5:75-86.
Not in our library10.1123/ijspp.5.1.75
235.
Moreira G.P., de Oliveira C.I.J., Greco P.J. Numerical superiority changes the physical demands of soccer players during small-sided games. Rev. Bras. Cineantropometria Desempenho Hum. 2015;17:269-279.
Not in our library
236.
Myers D. Relationship of anthropometric measurements and body composition to upper-body power in baseball players. Mo. J. Health Phys. Educ. Recreat. Dance. 2012;22:5-11.
Not in our library
237.
Myklebust H., Losnegard T., Hallén J. Differences in V1 and V2 ski skating techniques described by accelerometers. Scand. J. Med. Sci. Sports. 2014;24:882-893.
Not in our library10.1111/sms.12106
238.
Najafi B., Lee-Eng J., Wrobel J.S., Goebel R. Estimation of center of mass trajectory using wearable sensors during golf swing. J. Sports Sci. Med. 2015;14:354-363.
Not in our library
239.
Nakano G., Iino Y., Imura A., Kojima T. Transfer of momentum from different arm segments to a light movable target during a straight punch thrown by expert boxers. J. Sports Sci. 2014;32:517-523.
Not in our library10.1080/02640414.2013.843014
240.
Nedergaard N.J., Robinson M.A., Eusterwiemann E., Drust B., Lisboa P.J., Vanrenterghem J. The relationship between whole-body external loading and body-worn accelerometry during team-sport movements. Int. J. Sports Physiol. Perform. 2017;12:18-26.
Not in our library10.1123/ijspp.2015-0712
241.
Nedergaard N.J., Kersting U., Lake M. Using accelerometry to quantify deceleration during a high-intensity soccer turning manoeuvre. J. Sports Sci. 2014;32:1897-1905.
Not in our library10.1080/02640414.2014.965190
242.
Neville J.G., Rowlands D.D., Lee J.B., James D.A. A model for comparing over-ground running speed and accelerometer derived step rate in elite level athletes. IEEE Sens. J. 2016;16:185-191.
Not in our library10.1109/jsen.2015.2477497
243.
Norris M., Kenny I.C., Anderson R. Comparison of accelerometry stride time calculation methods. J. Biomech. 2016;49:3031-3034.
Not in our library10.1016/j.jbiomech.2016.05.029
244.
Nuzzo J.L., Anning J.H., Scharfenberg J.M. The reliability of three devices used for measuring vertical jump height. J. Strength Cond. Res. 2011;25:2580-2590.
Not in our library10.1519/jsc.0b013e3181fee650
245.
Connor S.O., McCaffrey N., Whyte E., Moran K. The novel use of a SenseCam and accelerometer to validate training load and training information in a self-recall training diary. J. Sports Sci. 2016;34:303-310.
Not in our library10.1080/02640414.2015.1050600
246.
Ohgi Y., Ichikawa H., Homma M., Miyaji C. Stroke phase discrimination in breaststroke swimming using a tri-axial acceleration sensor device. Sports Eng. 2003;6:113-123.
Not in our library10.1007/bf02903532
247.
Ohgi Y., Ichikawa H., Miyaji C. Microcomputer-based acceleration sensor device for swimming stroke monitoring. JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf. 2002;45:960-966.
Not in our library10.1299/jsmec.45.960
248.
Omkar S.N., Vanjare A.M., Suhith H., Kumar G.S. Motion analysis for short and long jump. Int. J. Perform. Anal. Sport. 2012;12:132-143.
Not in our library10.1080/24748668.2012.11868589
249.
Page R., Marrin K., Brogden C., Greig M. Biomechanical and physiological response to a contemporary soccer match-play simulation. J. Strength Cond. Res. 2015;29:2860-2866.
Not in our library10.1519/jsc.0000000000000949
250.
Pansiot J., Zhang Z., Lo B., Yang G. WISDOM: Wheelchair inertial sensors for displacement and orientation monitoring. Meas. Sci. Technol. 2011;22:105801.
Not in our library10.1088/0957-0233/22/10/105801
251.
Picerno P., Camomilla V., Capranica L. Countermovement jump performance assessment using a wearable 3D inertial measurement unit. J. Sports Sci. 2011;29:139-146.
Not in our library10.1080/02640414.2010.523089
252.
Picerno P., Iannetta D., Comotto S., Donati M., Pecoraro F., Zok M., Tollis G., Figura M., Varalda C., Di Muzio D., et al. 1RM prediction: A novel methodology based on the force-velocity and load-velocity relationships. Eur. J. Appl. Physiol. 2016;116:2035-2043.
Not in our library10.1007/s00421-016-3457-0
253.
Polgaze T., Dawson B., Hiscock D.J., Peeling P. A comparative analysis of acceleromter and time motion data in elite mens hockey training and competition. Int. J. Sports Physiol. Perform. 2015;10:446-451.
Not in our library10.1123/ijspp.2014-0233
254.
Praz C., Fasel B., Vuistiner P., Aminian K., Kayser B. Optimal slopes and speeds in uphill ski mountaineering: A laboratory study. Eur. J. Appl. Physiol. 2016;116:1011-1019.
Not in our library10.1007/s00421-016-3371-5
255.
Praz C., Fasel B., Vuistiner P., Aminian K., Kayser B. Optimal slopes and speeds in uphill ski mountaineering: A field study. Eur. J. Appl. Physiol. 2016;116:2017-2024.
Not in our library10.1007/s00421-016-3455-2
256.
Pruyn E.C., Watsford M.L., Murphy A.J. Differences in lower-body stiffness between levels of netball competition. J. Strength Cond. Res. 2015;29:1197-1202.
Not in our library10.1519/jsc.0000000000000418
257.
Requena B., Garcia I., Requena F., Bressel E., Saez-Saez de Villarreal E., Cronin J. Association between traditional standing vertical jumps and a soccer-specific vertical jump. Eur. J. Sport Sci. 2014;14:S398-S405.
Not in our library10.1080/17461391.2012.708790
258.
Requena B., Requena F., García I., de Villarreal E.S.S., Pääsuke M. Reliability and validity of a wireless microelectromechanicals based system (KeimoveTM) for measuring vertical jumping performance. J. Sports Sci. Med. 2012;11:115-122.
Not in our library
259.
Ritchie D., Hopkins W.G., Buchheit M., Cordy J., Bartlett J.D. Quantification of training and competition load across a season in an elite Australian Football club. Int. J. Sports Physiol. Perform. 2016;11:474-479.
Not in our library10.1123/ijspp.2015-0294
260.
Robinson M.G., Holt L.E., Pelham T.W., Furneaux K. Accelerometry measurements of sprint kayaks: The coaches’ new tool. Int. J. Coach. Sci. 2011;5:45-56.
Not in our library
261.
Rontu J.P., Hannula M.I., Leskinen S., Linnamo V., Salmi J.A. One-repetition maximum bench press performance estimated with a new accelerometer method. J. Strength Cond. Res. 2010;24:2018-2025.
Not in our library10.1519/jsc.0b013e3181c7c433
262.
Rowlands A.V., Stone M.R., Eston R.G. Influence of speed and step frequency during walking and running on motion sensor output. Med. Sci. Sports Exerc. 2007;39:716-727.
Not in our library10.1249/mss.0b013e318031126c
263.
Rowlands D.D., James D.A., Lee J.B. Visualization of wearable sensor data during swimming for performance analysis. Sports Technol. 2013;6:130-136.
Not in our library10.1080/19346182.2013.867965
264.
Rowlands D.D., James D.A., Thiel D. Bowler analysis in cricket using centre of mass inertial monitoring. Sports Technol. 2009;2:39-42.
Not in our library10.1080/19346182.2009.9648497
265.
Ruben R.M., Molinari M.A., Bibbee C.A., Childress M.A., Harman M.S., Reed K.P., Haff G.G. The acute effects of an ascending squat protocol on performance during horizontal plyometric jumps. J. Strength Cond. Res. 2010;24:358-369.
Not in our library10.1519/jsc.0b013e3181cc26e0
266.
Sadi F., Klukas R. New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration. GPS Solut. 2013;17:139-152.
Not in our library10.1007/s10291-012-0267-7
267.
Sagawa K., Abo S., Tsukamoto T., Kondo I. Forearm trajectory measurement during pitching motion using an elbow-mounted sensor. J. Adv. Mech. Des. Syst. Manuf. 2009;3:299-311.
Not in our library10.1299/jamdsm.3.299
268.
Sakurai Y., Zenya F., Ishige Y. Automated identification and evaluation of subtechniques in classical-style roller skiing. J. Sports Sci. Med. 2014;13:651-657.
Not in our library
269.
Saponara S. Wearable biometric performance measurement system for combat sports. IEEE Trans. Instrum. Meas. 2017;66:2545-2555.
Not in our library10.1109/tim.2017.2677679
270.
Sarkka O., Nieminen T., Suuriniemi S., Kettunen L. Augmented inertial measurements for analysis of javelin throwing mechanics. Sports Eng. 2016;19:219-227.
Not in our library10.1007/s12283-016-0194-x
271.
Sato K., Sands W.A., Stone M.H. The reliability of accelerometry to measure weightlifting performance. Sports Biomech. 2012;11:524-531.
Not in our library10.1080/14763141.2012.724703
272.
Sato K., Smith S.L., Sands W.A. Validation of an accelerometer for measuring sport performance. J. Strength Cond. Res. 2009;23:341-347.
Not in our library10.1519/jsc.0b013e3181876a01
273.
Sbriccoli P., Camomilla V., Di Mario A., Quinzi F., Figura F., Felici F. Neuromuscular control adaptations in elite athletes: The case of top level karateka. Eur. J. Appl. Physiol. 2010;108:1269-1280.
Not in our library10.1007/s00421-009-1338-5
274.
Scanlan A.T., Wen N., Tucker P.S., Dalbo V.J. The relationships between Internal and external training load models during basketball. J. Strength Cond. Res. 2014;28:2397-2405.
Not in our library10.1519/jsc.0000000000000458
275.
Schaffert N., Mattes K. Designing an acoustic feedback system for on-water rowing training. Int. J. Comput. Sci. Sport. 2011;10:71-76.
Not in our library
276.
Schütte K.H., Maas E.A., Exadaktylos V., Berckmans D., Venter R.E., Vanwanseele B. Wireless tri-axial trunk accelerometry detects deviations in dynamic center of mass motion due to running-induced fatigue. PLoS ONE. 2015;10:e0141957.
Not in our library10.1371/journal.pone.0141957
277.
Scott B.R., Lockie R.G., Knight T.J., Clark A.C., De Jonge X.A.K.J. A comparison of methods to quantify the in-season training load of professional soccer players. Int. J. Sports Physiol. Perform. 2013;8:195-202.
Not in our library10.1123/ijspp.8.2.195
278.
Seifert L., Dovgalecs V., Boulanger J., Orth D., Hérault R., Davids K. Full-body movement pattern recognition in climbing. Sports Technol. 2014;7:166-173.
Not in our library10.1080/19346182.2014.968250
279.
Seifert L., Orth D., Boulanger J., Dovgalecs V., Hérault R., Davids K. Climbing skill and complexity of climbing wall design: Assessment of jerk as a novel indicator of performance fluency. J. Appl. Biomech. 2014;30:619-625.
Not in our library10.1123/jab.2014-0052
280.
Seifert L., Wattebled L., Orth D., L’Hermette M., Boulanger J., Davids K. Skill transfer specificity shapes perception and action under varying environmental constraints. Hum. Mov. Sci. 2016;48:132-141.
Not in our library10.1016/j.humov.2016.05.004
281.
Setuain I., Martinikorena J., Gonzalez-Izal M., Martinez-Ramirez A., Gómez M., Alfaro-Adrián J., Izquierdo M. Vertical jumping biomechanical evaluation through the use of an inertial sensor-based technology. J. Sports Sci. 2016;34:843-851.
Not in our library10.1080/02640414.2015.1075057
282.
Shepherd J.B., Wada T., Rowlands D., James D.A. A novel AHRS inertial sensor-based algorithm for wheelchair propulsion performance analysis. Algorithms. 2016;9:55.
Not in our library10.3390/a9030055
283.
Sinclair J., Toth J., Hobbs S.J. The influence of energy return and minimalist footwear on the kinetics and kinematics of depth jumping in relation to conventional trainers. Kinesiology. 2015;47:11-18.
Not in our library
284.
Siska L., Brodáni J., Stefanovský M., Todorov S. Basic reliability parameters of a boxing punch. J. Phys. Educ. Sport. 2016;16:241-244.
Not in our library
285.
Slawson S.E., Justham L.M., Conway P.P., Le-Sage T., West A.A. Characterizing the swimming tumble turn using acceleration data. J. Sports Eng. Technol. 2012;226:3-15.
Not in our library10.1177/1754337111428395
286.
Sleivert G., Taingahue M. The relationship between maximal jump-squat power and sprint acceleration in athletes. Eur. J. Appl. Physiol. 2004;91:46-52.
Not in our library10.1007/s00421-003-0941-0
287.
Smith R.M., Loschner C. Biomechanics feedback for rowing. J. Sports Sci. 2002;20:783-791.
Not in our library10.1080/026404102320675639
288.
Spratford W., Portus M., Wixted A., Leadbetter R., James D.A. Peak outward acceleration and ball release in cricket. J. Sports Sci. 2015;33:754-760.
Not in our library10.1080/02640414.2014.962577
289.
Squadrone R., Rodano R., Preatoni E. Comparison of velocity and power output data derived from an inertial based system and an optical encoder during squat lifts in a weight room setting. J. Sports Med. Phys. Fit. 2012;52:40-46.
Not in our library
290.
Stamm A., James D.A., Thiel D.V. Velocity profiling using inertial sensors for freestyle swimming. Sports Eng. 2013;16:1-11.
Not in our library10.1007/s12283-012-0107-6
291.
Staniak Z., Buśko K., Górski M., Pastuszak A. Accelerometer profile of motion of the pelvic girdle in breaststroke swimming. J. Hum. Kinet. 2016;52:147-156.
Not in our library10.1515/hukin-2016-0002
292.
Starrs P., Chohan A., Fewtrell D., Richards J., Selfe J. Biomechanical differences between experienced and inexperienced wheelchair users during sport. Prosthet. Orthot. Int. 2012;36:324-331.
Not in our library10.1177/0309364612448807
293.
Stetter B.J., Buckeridge E., Von Tscharner V., Nigg S.R., Nigg B.M. A novel approach to determine strides, ice contact, and swing phases during ice hockey skating using a single accelerometer. J. Appl. Biomech. 2016;32:101-106.
Not in our library10.1123/jab.2014-0245
294.
Stickford A.S.L., Chapman R.F., Johnston J.D., Stager J.M. Lower-leg compression, running mechanics, and economy in trained distance runners. Int. J. Sports Physiol. Perform. 2015;10:76-83.
Not in our library10.1123/ijspp.2014-0003
295.
Strohrmann C., Harms H., Kappeler-setz C., Tr G. Monitoring kinematic changes with fatigue in running using body-worn sensors. IEEE Trans. Inf. Technol. Biomed. 2012;16:983-990.
Not in our library10.1109/titb.2012.2201950
296.
Suarez-Arrones L., Arenas C., López G., Requena B., Terrill O., Mendez-Villanueva A. Positional differences in match running performance and physical collisions in men rugby sevens. Int. J. Sports Physiol. Perform. 2014;9:316-323.
Not in our library10.1123/ijspp.2013-0069
297.
Suarez-Arrones L., Portillo J., Pareja-Blanco F., De Villareal E.S., Sánchez-Medina L., Munguía-Izquierdo D. Match-play activity profile in elite women’s rugby union players. J. Strength Cond. Res. 2014;48:452-458.
Not in our library10.1519/jsc.0b013e3182999e2b
298.
Sullivan C., Bilsborough J.C., Cianciosi M., Hocking J., Cordy J., Coutts A.J. Match score affects activity profile and skill performance in professional Australian Football players. J. Sci. Med. Sport. 2014;17:326-331.
Not in our library10.1016/j.jsams.2013.05.001
299.
Sullivan C., Bilsborough J.C., Cianciosi M., Hocking J., Cordy J.T., Coutts A.J. Factors affecting match performance in professional australian football. Int. J. Sports Physiol. Perform. 2014;9:561-566.
Not in our library10.1123/ijspp.2013-0183
300.
Supej M. 3D measurements of alpine skiing with an inertial sensor motion capture suit and GNSS RTK system. J. Sports Sci. 2010;28:759-769.
Not in our library10.1080/02640411003716934
301.
Takano K., Li K.F. A multimedia tennis instruction system: Tracking and classifying swing motions. Int. J. Space Based Situated Comput. 2013;3:155-168.
Not in our library10.1504/ijssc.2013.056406
302.
Tan H., Wilson A.M., Lowe J. Measurement of stride parameters using a wearable GPS and inertial measurement unit. J. Biomech. 2008;41:1398-1406.
Not in our library10.1016/j.jbiomech.2008.02.021
303.
Tang W.-T., Zhang W.-Y., Huang C.-C., Young M.-S., Hwang I.-S. Postural tremor and control of the upper limb in air pistol shooters. J. Sports Sci. 2008;26:1579-1587.
Not in our library10.1080/02640410802287063
304.
Thomas O., Sunehag P., Dror G., Yun S., Kim S., Robards M., Smola A., Green D., Saunders P. Wearable sensor activity analysis using semi-Markov models with a grammar. Pervasive Mob. Comput. 2010;6:342-350.
Not in our library10.1016/j.pmcj.2010.01.002
305.
Thompson C.J., Bemben M.G. Reliability and comparability of the accelerometer as a measure of muscular power. Med. Sci. Sports Exerc. 1999;31:897-902.
Not in our library10.1097/00005768-199906000-00020
306.
Vales-Alonso J., Chaves-Dieguez D., Lopez-Matencio P., Alcaraz J.J., Parrado-Garcia F.J., Gonzalez-Castano F.J. SAETA: A smart coaching assistant for professional volleyball training. IEEE Trans. Syst. Man Cybern. Syst. 2015;45:1138-1150.
Not in our library10.1109/tsmc.2015.2391258
307.
Van der Slikke R.M.A., Berger M.A.M., Bregman D.J.J., Lagerberg A.H., Veeger H.E.J. Opportunities for measuring wheelchair kinematics in match settings; reliability of a three inertial sensor configuration. J. Biomech. 2015;48:3398-3405.
Not in our library10.1016/j.jbiomech.2015.06.001
308.
Van der Slikke R.M.A., Berger M.A.M., Bregman D.J.J., Veeger H.E.J. From big data to rich data: The key features of athlete wheelchair mobility performance. J. Biomech. 2016;49:3340-3346.
Not in our library10.1016/j.jbiomech.2016.08.022
309.
Villaseñor A., Turcotte R.A., Pearsall D.J. Recoil effect of the ice hockey stick during a slap shot. J. Appl. Biomech. 2006;22:202-211.
Not in our library10.1123/jab.22.3.202
310.
Vingren J.L., Buddhadev H.H., Hill D.W. Smith machine counterbalance system affects measures of maximal bench press throw performance. J. Strength Cond. Res. 2011;25:1951-1956.
Not in our library10.1519/jsc.0b013e31821eb67f
311.
Waldron M., Worsfold P.R., Twist C., Lamb K. A three-season comparison of match performances among selected and unselected elite youth rugby league players. J. Sports Sci. 2014;32:1110-1119.
Not in our library10.1080/02640414.2014.889838
312.
Waldron M., Worsfold P., Twist C., Lamb K. Concurrent validity and test-retest reliability of a global positioning system (GPS) and timing gates to assess sprint performance variables. J. Sports Sci. 2011;29:1613-1619.
Not in our library10.1080/02640414.2011.608703
313.
Warlop T.B., Bollens B., Crevecoeur F., Detrembleur C., Lejeune T.M. Dynamics of revolution time variability in cycling pattern: Voluntary intent can alter the long-range autocorrelations. Ann. Biomed. Eng. 2013;41:1604-1612.
Not in our library10.1007/s10439-013-0834-2
314.
Watanabe K., Hokari M. Kinematical analysis and measurement of sports form. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2006;36:549-557.
Not in our library10.1109/tsmca.2005.855777
315.
Watari R., Hettinga B., Osis S., Ferber R. Validation of a torso-mounted accelerometer for measures of vertical oscillation and ground contact time during treadmill running. J. Appl. Biomech. 2016;32:306-310.
Not in our library10.1123/jab.2015-0200
316.
Weaving D., Marshall P., Earle K., Nevill A.M., Abt G. Combining internal- and external-training-load measures in professional rugby league. Int. J. Sports Physiol. Perform. 2014;9:905-912.
Not in our library10.1123/ijspp.2013-0444
317.
Wellman A.D., Coad S.C., Goulet G.C., McLellan C.P. Quantification of competitive game demands of NCAA division I college football players using global positioning systems. J. Strength Cond. Res. 2016;30:11-19.
Not in our library10.1519/jsc.0000000000001206
318.
Weyand P.G., Kelly M., Blackadar T., Darley J.C., Oliver S.R., Ohlenbusch N.E., Joffe S.W., Hoyt R.W. Ambulatory estimates of maximal aerobic power from foot-ground contact times and heart rates in running humans. J. Appl. Physiol. 2001;91:451-458.
Not in our library10.1152/jappl.2001.91.1.451
319.
White A., MacFarlane N. Analysis of international competition and training in men’s field hockey by global positioning system and inertial sensor technology. J. Strength Cond. Res. 2015;29:137-143.
Not in our library10.1519/jsc.0000000000000600
320.
Winter S.C., Lee J.B., Leadbetter R.I., Gordon S.J. Validation of a single inertial sensor for measuring running kinematics overground during a prolonged run. J. Fit. Res. 2016;5:14-23.
Not in our library
321.
Wixted A.J., Billing D.C., James D.A. Validation of trunk mounted inertial sensors for analysing running biomechanics under field conditions, using synchronously collected foot contact data. Sports Eng. 2010;12:207-212.
Not in our library10.1007/s12283-010-0043-2
322.
Wixted A., Portus M., Spratford W., James D. Detection of throwing in cricket using wearable sensors. Sports Technol. 2011;4:134-140.
Not in our library10.1080/19346182.2012.725409
323.
Wright B.V., Stager J.M. Quantifying competitive swim training using accelerometer-based activity monitors. Sports Eng. 2013;16:155-164.
Not in our library10.1007/s12283-013-0123-1
324.
Wundersitz D.W.T., Gastin P.B., Robertson S., Davey P.C., Netto K.J. Validation of a trunk-mounted accelerometer to measure peak impacts during team sport movements. Int. J. Sports Med. 2015;36:742-746.
Not in our library10.1055/s-0035-1547265
325.
Wundersitz D.W.T., Gastin P.B., Richter C., Robertson S.J., Netto K.J. Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running. Eur. J. Sport Sci. 2015;15:382-390.
Not in our library10.1080/17461391.2014.955131
326.
Wundersitz D.W.T., Josman C., Gupta R., Netto K.J., Gastin P.B., Robertson S. Classification of team sport activities using a single wearable tracking device. J. Biomech. 2015;48:3975-3981.
Not in our library10.1016/j.jbiomech.2015.09.015
327.
Wunsch T., Kröll J., Stöggl T., Schwameder H. Effects of a structured midsole on spatio-temporal variables and running economy in overground running. Eur. J. Sport Sci. 2017;17:303-309.
Not in our library10.1080/17461391.2016.1253776
328.
Yang S., Mohr C., Li Q. Ambulatory running speed estimation using an inertial sensor. Gait Posture. 2011;34:462-466.
Not in our library10.1016/j.gaitpost.2011.06.019
329.
Yiou E., Do M.C. In a complex sequential movement, what component of the motor program is improved with intensive practice, sequence timing or ensemble motor learning? Exp. Brain Res. 2001;137:197-204.
Not in our library10.1007/s002210000624
330.
Zhang Y., Chen K., Yi J. Rider trunk and bicycle pose estimation with fusion of force/inertial sensors. IEEE Trans. Biomed. Eng. 2013;60:2541-2551.
Not in our library10.1109/tbme.2013.2260339
331.
Zhang Y., Chen K., Yi J., Liu T., Pan Q. Whole-body pose estimation in human bicycle riding using a small set of wearable sensors. IEEE/ASME Trans. Mechatron. 2016;21:163-174.
Not in our library10.1109/tmech.2015.2490118
332.
Zhao Y., Gerhard D., Barden J. Periodicity-based swimming performance feature extraction and parameter estimation. Sports Eng. 2015;18:177-189.
Not in our library10.1007/s12283-015-0178-2
333.
Zihajehzadeh S., Loh D., Lee T.J., Hoskinson R., Park E.J. A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications. Measurement. 2015;73:200-210.
Not in our library10.1016/j.measurement.2015.05.023
334.
Zorko M., Nemec B., Babič J., Lešnik B., Supej M. The waist width of skis influences the kinematics of the knee joint in alpine skiing. J. Sports Sci. Med. 2015;14:606-619.
Not in our library
335.
Yuan Q., Chen I.M. Human velocity and dynamic behavior tracking method for inertial capture system. Sens. Actuators A Phys. 2012;183:123-131.
Not in our library10.1016/j.sna.2012.06.003
336.
Kelly S.J., Murphy A.J., Watsford M.L., Austin D., Rennie M. Reliability and validity of sports accelerometers during static and dynamic testing. Int. J. Sports Physiol. Perform. 2015;10:106-111.
Not in our library10.1123/ijspp.2013-0408
337.
Abernethy P., Wilson G., Logan P. Strength and power assessment. Issues, controversies and challenges. Sports Med. 1995;19:401-417.
Not in our library10.2165/00007256-199519060-00004
338.
Baker D., Wilson G., Carlyon B. Generality versus specificity: A comparison of dynamic and isometric measures of strength and speed-strength. Eur. J. Appl. Physiol. Occup. Physiol. 1994;68:350-355.
Not in our library10.1007/bf00571456
339.
Bosco C., Belli A., Astrua M., Tihanyi J., Pozzo R., Kellis S., Tsarpela O., Foti C., Manno R., Tranquilli C. A dynamometer for evaluation of dynamic muscle work. Eur. J. Appl. Physiol. Occup. Physiol. 1995;70:379-386.
Not in our library10.1007/bf00618487
340.
A six stage operational framework for individualising injury risk management in sport · 2017
Not checked
341.
Monitoring Training Load to Understand Fatigue in Athletes · 2014
No notice
342.
#SportTechieTrends: 80+ Industry Experts Share the Sports Technology Trends and Innovations to Watch in 2017.
Not in our library
343.
Statistics & Facts on Wearable Technology.
Not in our library
344.
Zervos H., Chansin G., Hayward J. Wearable Technology 2017–2027: Markets, Players, Forecasts: IDTechEx. 2017.
Not in our library
345.
Bughin L., Chui M., Manyika J. Ten IT-Enabled Business Trends for the Decade Ahead. 2013.
Not in our library
346.
Fuss F.K. Instrumentation of athletes and equipment during competitions. Sports Technol. 2009;1:235-236.
Not in our library10.1080/19346182.2008.9648479
347.
Lightman K. Silicon gets sporty. IEEE Spectr. 2016;53:48-53.
Not in our library10.1109/mspec.2016.7420400
348.
Hynes G., O’Grady M., O’Hare G. Towards accessible technologies for coaching. Int. J. Sports Sci. Coach. 2013;8:105-114.
Not in our library10.1260/1747-9541.8.1.105
349.
Mooney R., Corley G., Godfrey A., Osborough C., Newell J., Quinlan L.R., ÓLaighin G. Analysis of swimming performance: Perceptions and practices of US-based swimming coaches. J. Sports Sci. 2016;34:997-1005.
Not in our library10.1080/02640414.2015.1085074
350.
Ringuet-Riot C.J., Hahn A., James D.A. A structured approach for technology innovation in sport. Sports Technol. 2013;6:137-149.
Not in our library10.1080/19346182.2013.868468
351.
Baca A., Dabnichki P., Heller M., Kornfeind P. Ubiquitous computing in sports: A review and analysis. J. Sports Sci. 2009;27:1335-1346.
Not in our library10.1080/02640410903277427
352.
Ringuet-Riot C., Carter S., James D.A. Programmed innovation in team sport using needs driven innovation. Procedia Eng. 2014;72:817-822.
Not in our library10.1016/j.proeng.2014.06.139
353.
Zok M. Inertial sensors are changing the games. Proceedings of the 2014 International Symposium on Inertial Sensors and Systems (ISISS). 1-3.
Not in our library
354.
Wells D., Cereatti A., Camomilla V., Donnelly C., Elliott B., Alderson J. A calibration procedure for MIMU sensors allowing for the calculation of elbow angles. Proceedings of the 33rd Annual Conference of the International Society of Biomechanics in Sport. Volume 33;1326-1329.
Not in our library
355.
Hummel O., Fehr U., Ferger K. Beyond ibeer-exploring the potential of smartphone sensors for performance diagnostics in sports. Int. J. Comput. Sci. Sport. 2013;12:46-60.
Not in our library
356.
Oliveira D.S., Afonso J.A., Ao S., Chan A.H., Katagiri H., Xu L. A smartphone-based multi-sensor wireless platform for cycling performance monitoring. IAENG Transactions on Engineering Sciences: Special Issue for the International Association of Engineers Conferences 2015. 2016;322-335.
Not in our library
357.
Baca A., Kornfeind P., Preuschl E., Bichler S., Tampier M., Novatchkov H. A server-based mobile coaching system. Sensors. 2010;10:10640-10662.
Not in our library10.3390/s101210640
358.
Andreoni G., Standoli C.E., Paolo P.P. Defining requirements and related methods for designing sensorized garments. Sensors. 2016;16:769.
Not in our library10.3390/s16060769
359.
Vasilyev P., Pearson S., El-Gohary M., Aboy M., McNames J. Inertial and time-of-arrival ranging sensor fusion. Gait Posture. 2017;54:1-7.
Not in our library10.1016/j.gaitpost.2017.02.011
360.
Sabatini A., Genovese V. A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements. Sensors. 2014;14:13324-13347.
Not in our library10.3390/s140813324
361.
Son Y., Oh S. A barometer-IMU fusion method for vertical velocity and height estimation. Proceedings of the 2015 IEEE SENSORS. 1-4.
Not in our library
362.
Liebermann D.G., Katz L., Hughes M.D., Bartlett R.M., McClements J., Franks I.M. Advances in the application of information technology to sport performance. J. Sports Sci. 2002;20:755-769.
Not in our library10.1080/026404102320675611
363.
Umek A., Zhang Y., Tomažič S., Kos A. Suitability of strain gage sensors for integration into smart sport equipment: A golf club example. Sensors. 2017;17:916.
Not in our library10.3390/s17040916
364.
Vales-Alonso J., Ĺopez-Matencio P., Gonzalez-Castãno F.J., Navarro-Helĺin H., Bãnos-Guirao P.J., Perez-Martinez F.J., Martinez-Álvarez R.P., González-Jiménez D., Gil-Castiñeira F., Duro-Ferńandez R. Ambient intelligence systems for personalized sport training. Sensors. 2010;10:2359-2385.
Not in our library10.3390/s100302359
365.
James D.A., Fanella L., Cusani R. Near Real Time Network Simulation for Team Sports Monitoring. Procedia Eng. 2013;60:422-427.
Not in our library10.1016/j.proeng.2013.07.049
366.
Ghasemzadeh H., Barnes J., Guenterberg E., Jafari R. A phonological expression for physical movement monitoring in body sensor networks. Proceedings of the 2008 5th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2008). 58-68.
Not in our library
367.
Baca A. Adaptive systems in sports. Social Networks and the Economics of Sports. 2014;115-124.
Not in our library
368.
Cormie P., McBride J.M., McCaulley G.O. Validation of power measurement techniques in dynamic lower body resistance exercises. J. Appl. Biomech. 2007;23:103-118.
Not in our library10.1123/jab.23.2.103
369.
Lames M., McGarry T. On the search for reliable performance indicators in game sports. Int. J. Perform. Anal. Sport. 2007;7:62-79.
Not in our library10.1080/24748668.2007.11868388
370.
James D.A., Lee J.B. The increasing adoption of consumer grade wearables: Comparing the apples and oranges of sport science. J. Fit. Res. 2016;5:3-4.
Not in our library
371.
Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks · 2014
No notice
372.
Picerno P., Cereatti A., Cappozzo A. A spot check for assessing static orientation consistency of inertial and magnetic sensing units. Gait Posture. 2011;33:373-378.
Not in our library10.1016/j.gaitpost.2010.12.006
373.
Syed Z.F., Aggarwal P., Goodall C., Niu X., El-Sheimy N. A new multi-position calibration method for MEMS inertial navigation systems. Meas. Sci. Technol. 2007;18:1897-1970.
Not in our library10.1088/0957-0233/18/7/016
374.
Fong W.T., Ong S.K., Nee A.Y.C. Methods for in-field user calibration of an inertial measurement unit without external equipment. Meas. Sci. Technol. 2008;19:85202.
Not in our library10.1088/0957-0233/19/8/085202
375.
Olivares A., Olivares G., Gorriz J.M., Ramirez J. High-efficiency low-cost accelerometer-aided gyroscope calibration. Proceedings of the International Conference on Test and Measurement. 354-360.
Not in our library
376.
Gebre-Egziabher D., Elkaim G.H., Powell J.D., Parkinson B.W. A non-linear, two-step estimation algorithm for calibrating solid-state strapdown magnetometers. Proceedings of the 8th International Conference on Integrated Navigation Systems. 290-297.
Not in our library
377.
Lüken M., Misgeld B., Rüschen D., Leonhardt S. Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems. Sensors. 2015;15:25919-25936.
Not in our library10.3390/s151025919
378.
Winter D.A., Winter D. Biomechanics and Motor Control of Human Movement. 1990.
Not in our library
379.
De Vries W.H.K., Veeger H.E.J., Baten C.T.M., van der Helm F.C.T. Magnetic distortion in motion labs, implications for validating inertial magnetic sensors. Gait Posture. 2009;29:535-541.
Not in our library10.1016/j.gaitpost.2008.12.004

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Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review · 2019
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