Sensorimotor Closed-Loop Control in Post-Stroke Wrist-Hand Rehabilitation Robots: A Structured Narrative Review
TL;DR
Future wrist-hand systems should integrate somatosensory feedback, voluntary effort recognition, adaptive robotic/FES/NMES assistance, objective neurophysiological and kinematic assessment, home monitoring, and therapist-in-the-loop supervision, while avoiding claims of clinical superiority before stronger controlled evidence is available.
Abstract
Post-stroke wrist-hand rehabilitation robots can increase training intensity, repetition, and measurement. Still, systems centered mainly on mechanical assistance may not fully address the sensorimotor impairments that constrain functional recovery. This structured narrative review examines how sensorimotor closed-loop control may reshape wrist-hand robotic rehabilitation by linking somatosensory feedback, voluntary motor effort recognition, and home-based translation. The review was informed by a PubMed-based literature pool and organized around three interrelated themes rather than a meta-analytic effect estimate. Current evidence suggests that proprioceptive, tactile, vibrotactile, and electro-vibro-feedback strategies may support sensorimotor integration. At the same time, EMG-, EEG/BCI-, and CMC-informed approaches can help detect residual motor intention or effort and trigger robotic, FES, or NMES assistance. Home-based and telerehabilitation systems also show potential to improve access, training dose, and therapist-supported monitoring. However, the evidence remains heterogeneous, often limited by small samples, pilot designs, mixed devices, and incomplete validation of long-term home use. Future wrist-hand systems should integrate somatosensory feedba ck, voluntary effort recognition, adaptive robotic/FES/NMES assistance, objective neurophysiological and kinematic assessment, home monitoring, and therapist-in-the-loop supervision, while avoiding claims of clinical superiority before stronger controlled evidence is available.