Design and research of a wearable system for spinal deformity rehabilitation based on curvature and electromyography sensors

Abnormal spinal curvature poses a significant health risk to children and adolescents, highlighting the importance of maintaining good posture for spinal health. However, current posture detection methods face challenges such as insufficient detection accuracy, discomfort during use, and overall inconvenience. This study aims to design and implement a wearable system that integrates angle sensors and surface electromyography (sEMG) sensors to monitor spinal alignment and muscle activity in real-time. The system consists of a smart vest equipped with flexible bending sensors and sEMG electrodes, a microcontroller unit for data processing, and a mobile application for data visualization and user feedback. To validate the system's effectiveness, the study involved 65 eligible undergraduate students wearing the device, collecting sEMG signals under different posture conditions, and conducting posture analysis to evaluate the device's performance. The results showed a significant correlation between asymmetric paraspinal muscle activity and the direction of the spinal curve, with the system achieving an accuracy rate of 90.8% in predicting the curve direction using a logistic regression model. Although the system verifies spinal alignment through indirect measurement rather than direct imaging, the high consistency between sEMG asymmetry and expected biomechanical patterns supports its reliability as a functional screening tool. Wearable devices can provide timely posture correction reminders and personalized rehabilitation plans, offering a noninvasive, user-friendly solution for the prevention and rehabilitation of spinal curvature abnormalities.