Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison

Background Emerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities. Nowadays, motor rehabilitation exercises can be easily performed and monitored even at home by a variety of motion-tracking systems. These are cheap, reliable, easy-to-use, and allow also remote configuration and control of the rehabilitation programs. The two most promising technologies for home-based motor rehabilitation programs are inertial wearable sensors and video-based motion capture systems. Methods In this paper, after a thorough review of the relevant literature, an original experimental analysis is reported for two corresponding commercially available solutions, a wearable inertial measurement unit and the Kinect, respectively. For the former, a number of different algorithms for rigid body pose estimation from sensor data were also tested. Both systems were compared with the measurements obtained with state-of-the-art marker-based stereophotogrammetric motion analysis, taken as a gold-standard, and also evaluated outside the lab in a home environment. Results The results in the laboratory setting showed similarly good performance for the elementary large motion exercises, with both systems having errors in the 3–8 degree range. Usability and other possible limitations were also assessed during utilization at home, which revealed additional advantages and drawbacks for the two systems. Conclusions The two evaluated systems use different technology and algorithms, but have similar performance in terms of human motion tracking. Therefore, both can be adopted for monitoring home-based rehabilitation programs, taking adequate precautions however for operation, user instructions and interpretation of the results.

Abstract licence: CC BY-NC-ND

1. 10.

   The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications · 2006

2. 28.

   IMU-Based Joint Angle Measurement for Gait Analysis · 2014

3. 29.

   Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks · 2014

4. 32.

   Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review · 2015

5. 33.

   Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls · 2012

35 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.