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

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 mult

SUBMITTER: Arnold J 

PROVIDER: S-EPMC12317069 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature