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Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
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Towards the Development of a Learning-Based Intention Classification Framework for Pushrim-Activated Power-Assisted

Mahsa Khalili, Tianxin Tao, Ruolan Ye

    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |August 4, 2019
    PubMed
    Summary

    Researchers developed a user-intention detection framework to improve control of power-assist devices for manual wheelchairs (MWCs). This system accurately identifies wheelchair movements, enhancing user experience with powered wheels.

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    Area of Science:

    • Rehabilitation Engineering
    • Human-Computer Interaction
    • Robotics

    Background:

    • Manual wheelchairs (MWCs) require significant physical exertion.
    • Power assist devices, like pushrim-activated power-assisted wheels (PAPAWs), aim to reduce this load.
    • Current PAPAW controllers may not accurately interpret user intentions, leading to maneuvering difficulties.

    Purpose of the Study:

    • To analyze wheelchair propulsion dynamics with manual versus powered wheels.
    • To design and evaluate a user-intention detection framework for PAPAWs.
    • To improve the control and user experience of powered assist devices for manual wheelchairs.

    Main Methods:

    • Calculated wheelchair linear and angular velocity from wheel angular velocity data.
    • Collected kinematic data from manual wheelchair experiments.
    • Tested six supervised learning algorithms for classifying user movements (not moving, straight, left turn, right turn).

    Main Results:

    • Identified suboptimal design in current powered wheel controllers.
    • All tested supervised learning algorithms achieved high accuracy in detecting wheelchair movement types.
    • Classification of movements was completed with low computational time.

    Conclusions:

    • The proposed user-intention detection framework accurately classifies MWC movements.
    • This framework can inform the development of adaptive, learning-based controllers for PAPAWs.
    • Implementing individualized control strategies can enhance the usability and experience of PAPAW users.