Accurate human motion classification is required in different fields, which currently mainly relies on WiFi and millimeter waves. Meanwhile, the Ultra-Wideband (UWB) signal offers high spatiotemporal resolution and robust interference resistance, making it suitable for distinguishing limb motions from other body movements, such as body tremors, heartbeat, and respiration. This paper proposes a simple but effective method for arm motion classification and recognition using an Impulse Radio UWB radar. We simplify the motion feature data by extracting the Peak Amplitude Curve (PAC) from the obtained time-frequency spectrum, resulting in reduced dimensionality and sample size. By employing traditional machine learning models instead of complex deep learning models, we achieve a recognition accuracy of up to 94.7%. To demonstrate the reliability of the model, we conducted ten-fold cross-validation, which yielded an average recognition accuracy of 93%.


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    Titel :

    Peak Amplitude Curve Based Arm Motion Recognition Using IR-UWB Radar


    Beteiligte:
    Lin, Guiping (Autor:in) / Men, Jing (Autor:in) / Lin, Enmin (Autor:in) / Zhuang, Zhihao (Autor:in) / Zhang, Tingting (Autor:in)


    Erscheinungsdatum :

    2023-10-10


    Format / Umfang :

    2433309 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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