Document Type : Original Article
Authors
1
1Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt.
2
Department of Rheumatology and Rehabilitation, Faculty of Medicine, Minia University
3
Department of Production Engineering and Mechanical Design, Faculty of Engineering, Minia University, Minia , Egypt.
4
Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt
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 camera-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 between the system, patients, and healthcare providers.
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