Pass-On Control: Personalized Assistive Online Control for a Wrist Exosuit

We present a multimodal control system for a tendon-driven soft wrist exosuit that delivers personalized, angle- and mode-sensitive assistance during wrist flexion-extension. Our hierarchical architecture combines surface electromyography (sEMG) and inertial measurements through a three-layer controller comprising an intent recognizer, an angle-specific EMG normalization layer, and a low-level impedance controller. In a pilot study with four healthy participants performing repetitive wrist tasks at slow and fast speeds, with and without a 2.5 kg load, our controller substantially compensated the additional external load when present. Compared with unassisted trials under load, median normalized extensor activity decreased by approximately 30% (Cohen’s d > 1.7), and the fatigue-related drift in EMG across repetitions was eliminated. In contrast, assistance slightly increased EMG in the no-load condition. Assistance introduced a modest accuracy penalty: root-mean-square error and peak error rose by roughly 1–2° but remained below 4° on average. These results demonstrate that integrating angle-dependent EMG normalization with inertial sensing enables effective, task-specific unloading of the wrist while maintaining functional accuracy. Our findings lay the groundwork for larger studies and clinical applications of soft wrist exosuits.