Impact of Mixed Reality-Based Rehabilitation on Muscle Activity in Lower-Limb Amputees: An EMG Analysis

Abstract

The rehabilitation of amputees has gained research attention due to digital rehabilitation systems. Traditional methods rely on intensive hospital training and prosthetics, which have limitations in accessibility and sustainability. This study proposes a mixed reality (MR) system combining real and virtual environments to enhance effectiveness and provide immediate patient feedback. The system enables amputees to perform exercises outside the hospital, ensuring continuous rehabilitation. Surface electromyography (sEMG) analysis monitors muscle activity and synchronizes intended movements with virtual prosthetics in real-time. Various algorithms filter noise from sEMG signals and reproduce amputees' intended movements. Pre-processing includes amplification, mean filtering, and Savitzky-Golay filtering for signal quality improvement. A gated recurrent unit model with four hidden layers and dropout techniques is used for motion classification. The sEMG data was collected over 12 weeks from non-amputees and amputees, achieving 92.81% accuracy and 0.231 loss rate. Clinical trials involved quadriceps strengthening exercises, resulting in 231.61% muscle activity improvement. The MR-based system promotes muscle activation, enhances training effectiveness, and improves quality of life. This study integrates MR and sEMG technologies to provide a patient-centric, effective, and sustainable solution, overcoming limitations of existing methods. The system facilitates the convergence of technology and medical services, contributing to personalized rehabilitation frameworks.