Personalized ML-based wearable robot control improves impaired arm function

James Arnold, Prabhat Pathak, Yichu Jin, David Pont-Esteban, Connor M. McCann, Carolin Lehmacher, John P. Bonadonna, Tanguy Lewko, Katherine M. Burke, Sarah Cavanagh, Lynn Blaney, Kelly Rishe, Tazzy Cole, Sabrina Paganoni, David Lin and Conor J. Walsh ()
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James Arnold: Harvard University
Prabhat Pathak: Harvard University
Yichu Jin: Harvard University
David Pont-Esteban: Harvard University
Connor M. McCann: Harvard University
Carolin Lehmacher: Harvard University
John P. Bonadonna: Harvard University
Tanguy Lewko: Harvard University
Katherine M. Burke: Massachusetts General Hospital
Sarah Cavanagh: Harvard University
Lynn Blaney: Massachusetts General Hospital
Kelly Rishe: Massachusetts General Hospital
Tazzy Cole: Harvard University
Sabrina Paganoni: Massachusetts General Hospital
David Lin: Massachusetts General Hospital
Conor J. Walsh: Harvard University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Portable wearable robots offer promise for assisting people with upper limb disabilities. However, movement variability between individuals and trade-offs between supportiveness and transparency complicate robot control during real-world tasks. We address these challenges by first developing a personalized ML intention detection model to decode user’s motion intention from IMU and compression sensors. Second, we leverage a physics-based hysteresis model to enhance control transparency and adapt it for practical use in real-world tasks. Third, we combine and integrate these two models into a real-time controller to modulate the assistance level based on the user’s intention and kinematic state. Fourth, we evaluate the effectiveness of our control strategy in improving arm function in a multi-day evaluation. For 5 individuals post-stroke and 4 living with ALS wearing a soft shoulder robot, we demonstrate that the controller identifies shoulder movement with 94.2% accuracy from minimal change in the shoulder angles (elevation: 3.4°, depression: 1.7°) and reduces arm-lowering force by 31.9% compared to a baseline controller. Furthermore, the robot improves movement quality by increasing their shoulder elevation/depression (17.5°), elbow (10.6°) and wrist flexion/extension (7.6°) ROMs; reducing trunk compensation (up to 25.4%); and improving hand-path efficiency (up to 53.8%).

Date: 2025
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DOI: 10.1038/s41467-025-62538-8

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