Multimodal control improves robotic arm therapy engagement and effort
A Flow-Theoretic Multimodal Control Framework Fusing Mental and Physical Engagement for Upper-Limb Rehabilitation
Robotics
Summary
Robot-assisted arm therapy often struggles to tell if someone is not moving because their muscles are weak or if they are just not paying attention. The authors introduced a new method that combines brain signals showing mental focus with muscle signals to better guide the robotic arm. This system adjusts the help it provides based on both mental and physical effort, encouraging patients to stay engaged and work harder. Tests showed it reduced energy use by the robot and kept people in a focused state much longer, which is important for recovery.
What this means in practice
- •For rehabilitation therapists: Use dual brain and muscle signal feedback to better tailor robotic assistance during upper-limb patient therapy sessions.
- •For robot control engineers: Develop exoskeleton control systems that dynamically adjust assistance by combining mental focus and physical effort data for more effective user interaction.
Authors
Jun Leng, Pengcheng Li, Hanze Wang, Shuxiang Guo, Junlin Jiang
Abstract
While Assist-as-Needed (AAN) control aims to maximize user participation in robot-assisted training, existing methods relying solely on physical metrics cannot distinguish muscular inability from mental disengagement, leading to suboptimal interventions. To address this ambiguity, a Flow-theoretic multimodal AAN framework is proposed, fusing EEG-based mental engagement and sEMG-based physical engagement into an adaptive impedance control strategy. A four-quadrant fuzzy decision engine translates these dual physiological metrics into continuous control policies. This enables the upper-limb exoskeleton to inject feedforward assistance during intent-execution mismatch (e.g., motor output attenuation despite mental focus) and escalate guiding stiffness to penalize mental distraction. The framework was validated with healthy participants against a state-of-practice sEMG-only AAN baseline. Kinematically and mechanically, the proposed framework reduced robotic energy expenditure by 71.9\% while maintaining precise trajectory tracking, confirming it elicited greater voluntary human effort. Physiologically, the system increased the time spent in the neuroplasticity zone (Flow State Ratio) from 4.6% to 42.8%. These results validate that decoupling and jointly regulating mental and physical states enhances the selectivity of robotic assistance, mitigating user passivity and sustaining the engagement crucial for motor learning.