Design of an Intelligent Control System for Motor Recovery of Hemiplegic Patients

Abstract

Stroke stands as a primary reason for prolonged motor dysfunction that produces long-term dependence and reduced use of upper limbs throughout everyday activities. The established rehabilitation techniques need ongoing therapist guidance together with clinical facility access which restricts their availability to numerous patients. This research introduces a voice-operated exoskeleton system which enables upper arm rehabilitation for stroke survivors at home. The core of the system features a lightweight convolutional neural network (CNN) model that operates under memory and processing constraints to perform real-time voice command detection of “Up” and “Down” for initiating therapeutic flexion and extension movements. The model framework was built to deliver optimal performance and minimize computational resources. The quantized CNN model achieved an accuracy of 96.87%. The system functions with complete offline and hands-free operation on resource-constrained low-power hardware. The study demonstrates that embedded CNN-based voice recognition systems can create scalable customizable rehabilitation technologies which enhance accessibility and effectiveness for patients needing limited clinical support.