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Related Experiment Video

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Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge

Seth R Donahue, Michael E Hahn

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |December 1, 2021
    PubMed
    Summary

    A heuristic algorithm and Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) accurately identified human gait events from low-frequency sensor data. Both methods show potential for locomotion mode prediction.

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    Area of Science:

    • Biomechanics
    • Wearable Technology
    • Machine Learning

    Background:

    • Accurate identification of gait events is crucial for understanding human locomotion.
    • Wearable sensors offer a feasible method for gait analysis outside laboratory settings.
    • Minimally sampled data presents challenges for precise gait event detection.

    Purpose of the Study:

    • To compare a heuristic algorithm with the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) for gait event identification.
    • To evaluate the predictive capability of these algorithms using minimally sampled human locomotion data.
    • To assess the lead time in identifying gait events prior to their occurrence.

    Main Methods:

    • Utilized data from 16 participants (21-64 years) with a single gyroscopic sensor.
    • Collected data across various locomotion modes: level walking, running, ramps, and stairs (≤ 100 Hz).
    • Compared a heuristic feature identification algorithm against BP-AR-HMM output.

    Main Results:

    • Heuristic algorithm achieved 94% accuracy in identifying initial contact (IC) and toe off (TO) across locomotion modes.
    • BP-AR-HMM achieved 99% accuracy in identifying impending IC and TO.
    • Lead times for heuristic algorithm: 186.32 ± 86.70 ms (IC), 63.96 ± 46.30 ms (TO).
    • Lead times for BP-AR-HMM: 59.41 ± 54.41 ms (IC), 90.79 ± 35.51 ms (TO).

    Conclusions:

    • Both heuristic and BP-AR-HMM approaches demonstrate high accuracy in gait event identification.
    • These methods are consistent and show potential for classifying and predicting locomotion modes.
    • The findings support the use of minimally sampled sensor data for advanced gait analysis.