Learning Therapist Policy from Therapist-Exoskeleton-Patient Interaction
Grayson Snyder, Lorenzo Vianello, Levi Hargrove, Matthew L. Elwin, Jose Pons

TL;DR
This paper introduces a machine learning framework that models and visualizes therapist responses during post-stroke gait therapy, enabling robot-assisted support that enhances therapy effectiveness and reduces therapist fatigue.
Contribution
It presents a novel approach combining VAE, GMM, and LSTM models to capture and predict therapist responses, improving robot-mediated therapy strategies.
Findings
Model accurately predicts therapist joint torques from patient kinematics.
Visualization of interaction dynamics informs therapy strategy and robot control.
Preliminary tests show potential for real-time exoskeleton assistance.
Abstract
Post-stroke rehabilitation is often necessary for patients to regain proper walking gait. However, the typical therapy process can be exhausting and physically demanding for therapists, potentially reducing therapy intensity, duration, and consistency over time. We propose a Patient-Therapist Force Field (PTFF) to visualize therapist responses to patient kinematics and a Synthetic Therapist (ST) machine learning model to support the therapist in dyadic robot-mediated physical interaction therapy. The first encodes patient and therapist stride kinematics into a shared low-dimensional latent manifold using a Variational Autoencoder (VAE) and models their interaction through a Gaussian Mixture Model (GMM), which learns a probabilistic vector field mapping patient latent states to therapist responses. This representation visualizes patient-therapist interaction dynamics to inform therapy…
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Taxonomy
TopicsStroke Rehabilitation and Recovery · Prosthetics and Rehabilitation Robotics · Balance, Gait, and Falls Prevention
