Revolutionizing Stroke Rehabilitation: Dynamic Glove-Based Rehabilitation System Empowered by CNN for Spastic Hands.

Abstract

Hand spasticity poses a significant challenge for stroke survivors, impacting hand functionality and hindering daily activities . The study introduces a smart rehabilitation system engineered for post-stroke hand spasticity. Comprising four units includes biometric measurement gloves, rehabilitation gloves, a camera, a telecom unit, and a computer unit . Biometric measurement gloves with sensors measure patient features. Data inputs include biometric measurements and cam-era-captured images. Computer programs consist of a clinical biometric program and a CNN program, specifically ResNet50 architecture . The telecom unit facilitates communication between the computer unit and rehabilittion gloves, doctor section, and patient section. The smart rehabilitation system offers advantages such as user-friendly operation, cost-effectiveness, elimination of physical visits to rehabilitation centers, and exceptional accuracy with a 99% validation accuracy rate and 0.0053 validation loss in the CNN framework. The clinical biometric program is used to analyze programs with high accuracy . This study presents an innovative rehabilitation system. It includes biometric measurement gloves for patient assessment and rehabilitation gloves for hand exercises. Two programs, a clinical biometric program, and an intelligent CNN-based program, diagnose and therapies based on biometric data and image analysis. The mobile application communicates be-tween the system