Cloud-Enabled Robotic Therapy for Spinal Cord Injuries Patients Using Neural Network Models

Abstract

Spinal cord injuries (SCI) need prolonged rehabilitation to restore motor abilities, yet traditional therapeutic approaches face challenges related to accessibility, consistency, and personalization. This research introduces a cloud-based robotic treatment system using neural network models to improve rehabilitation for spinal cord injury patients. The objective is to develop a cloud-based robotic treatment system using neural networks for adaptable, accurate, and efficient spinal cord rehabilitation. The system incorporates IoT-enabled robotic exoskeletons, real-time sensor data analysis, and adaptive AI-based therapeutic personalization. Neural networks assess patient motions, forecast recovery trajectories, and adapt treatment protocols to enhance rehabilitation results. The model integrates sensor fusion, adjustable learning rates, and real-time feedback mechanisms to improve precision. Data security is provided by end-to-end encryption, cloud integration, and secure cloud-based storage. The proposed approach enhances therapeutic accuracy, minimizes manual involvement, and facilitates remote surveillance. The performance assessment indicates superior accuracy, reduced loss rates, and enhanced rehabilitation efficiency relative to traditional methods. It highlights the promise of cloud-integrated, AI-driven robotic treatment in enhancing personalized and scalable rehabilitation options for spinal cord injury patients.