Skip to content
naulon
Not checkedNot checked for notices yet.

In-Home Floor Based Sensor System-Smart Carpet- to Facilitate Healthy Aging in Place (AIP)

Fadi Muheidat, Lo'Ai A. Tawalbeh

IEEE Access · 2020

doi:10.1109/access.2020.3027535 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

With the rapidly aging population, there is a need to detect elderly's activity, monitor their health, alert health care personnel, and provide real-time and long-term access to the generated sensor data. With the advances in sensor technologies, communication protocols, computing power, and cloud and edge services, it is now possible to build smart assistive living systems to improve people's lives. In this research, we present a Context-aware and private real-time reporting aging in place system. The proposed system, we call it smart carpet, consists of a sensor pad placed under a carpet; the electronics reads walking activity to provide an automated health monitoring and alert system. We extended the system's functionalities to improve its ability to detect falls, measure gait, and count the number of people traversing the carpet (socializing). In an urgent and time-sensitive situation, we need to provide a real-time notification. We propose a cooperative cloudlet model, where the sensors' data will be sent to the nearest Cloudlet for analysis and extracting real-time decisions in minimal delay. Results showed that our system could assist the elderly in detecting falls with 95% sensitivity and 85% specificity. Measuring and estimating their gait with a mean percentage error difference to GAITRite 1.43% in walking speed; hence, predicting a fall risk and counting people's plurality socializing with the elderly with an average accuracy of 100%. We evaluated our system's improvements in a controlled environment. We are looking forward to deploying the system in a nursing home (after the COVID -19 is over) for more data gathering and validations.

Abstract licence: CC BY-NC-ND

References55

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

    Reads

    Read by people on this page

    None yet.

    Checked or read by AI through naulon

    None yet.

    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.

    Are you an author?

    Is this your paper?

    Claim it to add your note and be paid when agents cite it. Free.