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Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.

Vladimir Joukov, Vincent Bonnet, Michelle Karg

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |February 1, 2017
    PubMed
    Summary

    A new Rhythmic Extended Kalman Filter (Rhythmic-EKF) uses wearable sensors to accurately estimate lower body pose during walking. This method improves physiotherapy by providing objective gait analysis and movement insights.

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    Area of Science:

    • Biomechanics
    • Wearable Technology
    • Robotics

    Background:

    • Accurate lower body pose estimation is crucial for physiotherapy and rehabilitation.
    • Existing methods often rely on intrusive markers or complex setups.
    • Objective, quantitative measures of movement are needed for effective patient assessment.

    Purpose of the Study:

    • To develop a non-intrusive method for estimating lower body pose using wearable sensors.
    • To extract objective performance measures for physiotherapy applications.
    • To improve pose estimation accuracy through adaptive, learned movement models.

    Main Methods:

    • Development of the Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm.
    • Utilizing small, wearable, wireless sensors for data acquisition.
    • Learning an individualized canonical dynamical system model of periodic movement.
    • Online estimation of pose, phase, and frequency for motion segmentation.

    Main Results:

    • The Rhythmic-EKF outperformed the standard Extended Kalman Filter (EKF) in simulations and real-world data.
    • Achieved 40% and 37% improvement in joint acceleration and velocity estimates, respectively, for healthy participants.
    • Estimated joint angles with a root mean squared error of 2.4°.
    • Segmented motion into repetitions with 96% accuracy.

    Conclusions:

    • The Rhythmic-EKF offers a robust and accurate solution for non-intrusive lower body pose estimation.
    • The approach provides objective gait analysis features valuable for physiotherapy.
    • This method holds promise for remote patient monitoring and personalized rehabilitation.