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Unsupervised Phase Learning and Extraction from Repetitive Movements.

Prayook Jatesiktat, Dollaporn Anopas, Wei Tech Ang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
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    This study introduces a universal, unsupervised learning method for extracting movement phases. It successfully identifies phases in rat gait and human limb movements without needing pre-labeled data.

    Area of Science:

    • Biomechanics
    • Robotics
    • Machine Learning

    Background:

    • Automatic phase extraction is vital for analyzing repetitive movements in fields like robotics and rehabilitation.
    • Current methods rely on handcrafted features, limiting their universality across different movements.
    • A need exists for a more adaptable and generalizable phase extraction technique.

    Purpose of the Study:

    • To develop a novel, unsupervised learning-based technique for universal phase extraction from repetitive movements.
    • To create a method that learns phase concepts directly from movement data without requiring pre-labeled phase information.
    • To demonstrate the technique's effectiveness across diverse biological movements.

    Main Methods:

    • Designed a neural network architecture specifically for unsupervised phase learning.

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  • Developed a custom cost function to guide the network in learning phase representations.
  • Applied the method to extract phases from recorded rat gait cycles and human upper limb movements.

    • Main Results:

    • Successfully extracted movement phases at the sample level for both rat and human datasets.
    • The technique demonstrated robustness against variations in movement speed, trajectory, and subject anthropometry.
    • Achieved accurate phase extraction without relying on any prior phase labels.

    Conclusions:

    • The proposed unsupervised learning approach offers a universal solution for phase extraction in repetitive movements.
    • This method overcomes the limitations of handcrafted features in existing techniques.
    • The demonstrated success in diverse biological movements highlights its broad applicability in robotics, rehabilitation, and biomechanics.