2025-08-19 ハーバード大学
Prabhat Pathak and James Arnold demonstrate the wearable robotic device in the lab.
<関連情報>
- https://seas.harvard.edu/news/2025/08/wearable-robot-learns
- https://www.nature.com/articles/s41467-025-62538-8
- https://medibio.tiisys.com/106379/
個人向けMLベースのウェアラブルロボット制御が障害のある腕の機能を改善する 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 & Conor J. Walsh
Nature Communications Published:02 August 2025
DOI:https://doi.org/10.1038/s41467-025-62538-8
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%).
