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%.


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    Titel :

    A Machine Learning Approach for Running Grade Classification Using IMUs Data


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Romdhane, Lotfi (Herausgeber:in) / Mlika, Abdelfattah (Herausgeber:in) / Zeghloul, Saïd (Herausgeber:in) / Chaker, Abdelbadia (Herausgeber:in) / Laribi, Med Amine (Herausgeber:in) / Chaker, Abdelbadia (Autor:in) / Atiga, Hanin (Autor:in) / Donahue, Seth (Autor:in) / Robinson, Rachel (Autor:in) / Chebbi, Aida (Autor:in)

    Kongress:

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


    Erschienen in:

    Robotics and Mechatronics ; Kapitel : 22 ; 239-248


    Erscheinungsdatum :

    25.09.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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