Personalized ML-based wearable robot control improves impaired arm function.

Journal: Nature communications
Published Date: Aug 2, 2025

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%).

Authors

  • James Arnold
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Prabhat Pathak
    Department of Physical Education, Seoul National University, Republic of Korea.
  • Yichu Jin
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • David Pont-Esteban
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Connor M McCann
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Carolin Lehmacher
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • John P Bonadonna
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Tanguy Lewko
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Katherine M Burke
    Neurological Clinical Research Institute, Massachusetts General Hospital, Boston, MA, USA.
  • Sarah Cavanagh
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Lynn Blaney
    Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
  • Kelly Rishe
    Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
  • Tazzy Cole
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
  • Sabrina Paganoni
    Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, 300 First Ave, Boston, MA, 02129, USA.
  • David Lin
    Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
  • Conor J Walsh
    John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA, 02138, USA. walsh@seas.harvard.edu.

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