A Machine Learning-Based Countermovement Performance Measurement Method Using a Wearable IMU

Yi Yu Chiang, Wen Yueh Shih, Wei Han Chen, Jiun Long Huang, Tzyy Yuang Shiang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    In the field of sports, there are many advanced technologies to help athletes improving their skills or monitoring their body health. The force plate is one of the devices which can be used to observe the status in neuromuscular function and fatigue of athletes by (1) asking them do the countermovement jump (CMJ) on it and (2) evaluating some performance indicators such as flight time and net pulse. However, the force plate is of low portability due to it high price and heavy weight. In this paper, we propose a machine learning-based method to measure CMJ performance with an inexpensive wearable inertial measurement unit (IMU). Based on the measured acceleration, we first extract some features and then adopt the machine learning algorithm to learn several models to estimate the above indicators, respectively. The experiments are conducted by 280 countermovement jumps performed by 14 healthy subjects. The experimental results show that the proposed system is of error rate less than 8%.

    Original languageEnglish
    Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages79-85
    Number of pages7
    ISBN (Electronic)9780738142623
    DOIs
    StatePublished - Dec 2020
    Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
    Duration: 3 Dec 20205 Dec 2020

    Publication series

    NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

    Conference

    Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
    Country/TerritoryTaiwan
    CityTaipei
    Period3/12/205/12/20

    Keywords

    • Countermovement jump
    • Force plate
    • IMU
    • Machine learning
    • Sport science
    • Wearable device

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