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Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
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Quantifying User Engagement During an Upper Limb Rehabilitation Task.

Yawen Zhang, Haofei Wang, Bertram E Shi

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |December 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a virtual reality robot-assisted system to measure patient engagement in rehabilitation. Behavioral signals, like eye blinks and gaze, proved more effective than physiological ones for estimating engagement.

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    Area of Science:

    • Robotics
    • Human-Computer Interaction
    • Rehabilitation Engineering

    Background:

    • Patient engagement is crucial for effective post-stroke robotic rehabilitation.
    • Limited research exists on modulating and quantifying engagement during therapy.

    Purpose of the Study:

    • To develop and evaluate a virtual reality (VR)-integrated robot-assisted system for upper limb rehabilitation.
    • To enable simultaneous modulation and monitoring of user engagement.
    • To investigate the effectiveness of physiological and behavioral indicators for engagement estimation.

    Main Methods:

    • A VR-integrated robot-assisted system was used for a line tracing task.
    • Task difficulty was modulated via shape complexity and force noise.
    • Engagement was estimated using physiological (GSR, pupil diameter) and behavioral (eye blink, gaze) signals.
    • A Game Engagement Questionnaire (GEQ) was used for benchmarking.
    • Twenty healthy subjects participated.

    Main Results:

    • Behavioral signals were more informative for predicting engagement than physiological signals.
    • An optimal 11-second analysis window was identified for accurate engagement metrics (MAE = 0.73, r = 0.42).
    • Peak engagement, aligning with flow theory, occurred when task difficulty matched user skill (Gaussian model: R2 = 0.76, RMSE = 0.18).

    Conclusions:

    • Behavioral measurements offer a reliable, non-invasive method for estimating engagement during rehabilitation tasks.
    • This approach supports the development of adaptive systems that adjust difficulty to optimize patient engagement.
    • Findings pave the way for enhanced robotic rehabilitation strategies.