The Design of a Speech Recognition-Based Finger Training Rehabilitation Device: Mechanical and System Aspects

Abstract

This research introduces a novel speech recognition-based control system for finger rehabilitation aimed at addressing the inefficiencies and prolonged recovery periods commonly associated with traditional stroke rehabilitation methods. The system enables stroke patients to engage in finger exercises while simultaneously providing real-time feedback on the finger's angle, speed, and position. This feedback is invaluable for rehabilitation physicians, offering critical data for assessing and refining rehabilitation strategies. The system architecture is composed of three main components: the hardware circuitry, a lower-level computer control system, and a voice recognition-based human-computer interaction system, all integrated with a finger movement perception system. Employing the Hidden Markov Model (HMM) for pattern recognition in the voice interaction component, the system has undergone simulation testing to verify its effectiveness. The findings confirm that the speech recognition-based rehabilitation training system meets all design expectations, providing a safe, reliable, and promising approach for enhancing finger rehabilitation practices.