This paper endeavors to advance Machine Learning techniques for biomechanical analysis through the classification of running grades. Employing data collected via three Inertial Measurement Unit (IMU) sensors, gait cycles were identified using a windowing technique, and a set of features from both time and frequency domain was identified and extracted. The methodology involved training and validating of three machine learning models, with performance assessed using standard metrics. Results revealed the superior performance of the quadratic support vector machine algorithm, boasting accuracy, recall, and precision metrics exceeding 99%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Machine Learning Approach for Running Grade Classification Using IMUs Data


    Additional title:

    Mechan. Machine Science



    Conference:

    IFToMM International Symposium on Robotics and Mechatronics ; 2024 ; Djerba, Tunisia April 17, 2024 - April 19, 2024


    Published in:

    Robotics and Mechatronics ; Chapter : 22 ; 239-248


    Publication date :

    2024-09-25


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    ProprioLogger: Evaluating Proprioception During Post-Surgical Rehabilitation using Portable IMUs

    Bellaire, Samuel / Abu-raddaha, Abdalmalek / Zaka, Kevin et al. | IEEE | 2024


    Model-Based Fusion of GNSS and Multiple-IMUs

    Sharma, Aman / Gilgien, Simon / Skaloud, Jan | IEEE | 2024


    Model of Launch Vehicle Dynamics and Redundant Strapdown IMUs

    Li, Xuefeng / Xu, Fan / Xu, Guoqiang | Springer Verlag | 2022