Abstract

Knee rehabilitation is essential for regaining joint range of motion and flexibility following an injury or surgery, and it frequently calls for weeks of physical therapy. Temporal delays that interfere with real-time feedback and lessen synchronization between patient movements and device actions are just two of the major drawbacks of current technologies. Longer recovery times and less than ideal patient care result from these difficulties, which are exacerbated by the absence of sophisticated control systems that can increase accuracy and response times. Knee-related conditions make up a significant percentage of musculoskeletal disorders, which are a major cause of disability worldwide. In order to satisfy the increasing demand for efficient rehabilitation solutions, these technological gaps must be filled. Potential solutions to these problems are offered by developments in machine learning, EMG signal classification, and Internet of Things-based control systems. This work aims to investigate methods to lower time delays, increase real-time responsiveness, and raise the performance of knee rehabilitation devices. Combining these developments seeks to maximize rehabilitation results and improve the quality of patient treatment. Emphasizing the need for real-time optimization and delay reduction to accommodate the rising prevalence of osteoarthritis and knee injuries worldwide, this paper addresses the clinical and technological issues of knee rehabilitation devices.