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Related Experiment Video

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Adaptive Change-Point Detection for Studying Human Locomotion.

Sylvain Jung, Laurent Oudre, Charles Truong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study introduces an adaptive change-point detection method for analyzing human gait and activity signals. The approach effectively segments inertial data, enabling refined analysis of locomotion phases in various environments.

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    Area of Science:

    • Biomechanics
    • Signal Processing
    • Human Locomotion Analysis

    Background:

    • Inertial signals are crucial for analyzing human gait and activity.
    • Current methods often lack adaptability for diverse protocols and environments.
    • Accurate segmentation of movement phases is essential for detailed analysis.

    Purpose of the Study:

    • To present an innovative, adaptive, and supervised change-point detection method for inertial signals.
    • To enable refined analysis of successive phases within gait protocols.
    • To facilitate automated and adaptive study of human gait and activity.

    Main Methods:

    • Utilized an adaptive and supervised change-point detection procedure.
    • Decomposed inertial signals into homogeneous segments.
    • Employed a training procedure for broad applicability across protocols and granularities.

    Main Results:

    • Tested on 15 healthy subjects performing a complex activity protocol.
    • Demonstrated promising results for automated and adaptive analysis of human gait.
    • Successfully isolated homogeneous phases for refined locomotion study.

    Conclusions:

    • The proposed method offers a novel approach to analyze human activity and locomotion.
    • It is applicable in semi-controlled and potentially Free-Living Environments (FLEs).
    • The adaptive change-point detection enhances the study of complex human movements.