Quantifying User Engagement During an Upper Limb Rehabilitation Task

Enhancing patient engagement is essential for effective post-stroke robotic rehabilitation, yet limited effort has been made towards modulating and quantifying patient engagement during therapy. To Bridge this gap, we introduce a virtual reality (VR)-integrated robot-assisted system for upper limb rehabilitation, which innovatively enables simultaneous modulation and monitoring of user engagement in a line tracing task. Modulation is governed by two parameters: shape complexity and force noise disturbance level. Our system estimates engagement using physiological (GSR and pupil diameter) and behavioral (eye blink and gaze) indicators, benchmarked against the Game Engagement Questionnaire (GEQ). The study involved twenty healthy right-handed subjects. Results show that behavioral signals are more informative in predicting engagement than physiological signals, which were the focus of most prior efforts in estimating engagement. Our detailed analysis identifies an optimal 11-second window-initiated no earlier than 15 seconds into the trial-that yields the most accurate engagement metrics for estimation (MAE = 0.73, r = 0.42). Consistent with Mihaly Csikszentmihalyi's flow theory, the estimated engagement is maximized when task difficulty matches the user's skill level, with peak engagement modeled as a Gaussian function (R2 = 0.76, RMSE = 0.18). Taken together, this study confirms the potential of behavioral measurements for reliable, non-invasive engagement estimation during task performance, paving the way for adaptive systems that automatically adjust task difficulty to enhance patient engagement throughout the rehabilitation process.