Design and Characterization of a Novel Compact Hand Exoskeleton Robot for Telerehabilitation and Muscle Spasticity Assessment

• Published in: IEEE/ASME transactions on mechatronics 2024-08, Vol.29 (4), p.2416-2427
• Main Authors: Lai, Jianwei, Song, Aiguo
• Format: Article
• Language: English
• Subjects: Algorithms; Contact angle; Contact force; DC motors; Exoskeletons; Fingers; Force; Gears; Hand (anatomy); Lightweight; Muscles; Patients; Portable equipment; Rehabilitation; Rehabilitation robot; Rehabilitation robots; Remote control; Robot sensing systems; Robots; Shafts; Spasticity; Synchronous motors; Therapists; underactuation; wearable robot; Weight reduction
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2023.3336313

Rehabilitation robots can aid patients in performing hand exercises in their own home. However, existing rehabilitation equipment is bulky and difficult to wear and carry, and therapists are unable to remotely assess a patient's finger muscle spasticity. This article describes a lightweight exoskeleton robot that facilitates hand rehabilitation exercises and enables muscle spasticity assessment at home. A hand exoskeleton with one degree of freedom assists patients in flexion and extension movements of their fingers. Its motor has a reduction ratio of 19:1, allowing passive back-driving. The exoskeleton's link lengths are determined by an optimization algorithm. The proposed device has a total weight of 0.356 kg and the torque of the dynamic structure to the metacarpophalangeal joint is 1.832 N \cdot m, reflecting its lightweight and portable nature. To aid remote assessments of patients' muscle spasticity, the exoskeleton is controlled using a finger tension feedback algorithm. This enables the patient's rehabilitation process to be managed remotely. Experiments involving ten patients and three therapists are conducted to evaluate the robot's feasibility. The results demonstrate that the robot can flex and extend the fingers with a mean angle error of 1.16^\circ and a mean contact force error of 0.25 N. Moreover, the robot achieves 75% accuracy in assisting therapists with remote assessment of the patient's finger muscle spasticity.