Detecting method of optimal exercise posture using autoencoder: Utilizing surface electromyography and textile stretch sensor

Abstract

This study aims to validate the optimal posture for the Dumbbell Biceps Curl (DBC) exercise using Textile stretch sensors and Surface Electromyography (sEMG), and then detect inaccurate posture using a Sparse Autoencoder. To validate the optimal DBC exercise posture, we measured the effects of wrist supination and the angle of the upper body and elbow on the biceps and forearm muscles. A wrist sleeve-shaped Textile stretch sensor detects wrist supination, and sEMG measures biceps and forearm muscle activation. The experiment results confirmed that an angle between the upper body and the elbow within 90°, coupled with wrist supination, constitutes the most efficient posture, maximizing the activation of the biceps while minimizing the synergistic effects of the forearm muscles. Subsequently, this posture was learned through a Sparse Autoencoder, and the Root Mean Square Error values of the trained model were lowest in the optimal posture (Biceps: 0.090, Forearm: 0.076). This suggests that Sparse Autoencoder could be useful in identifying inaccurate exercise postures. In summary, this study aims to develop an exercise posture feedback system through the integration of modern exercise physiology and technology, particularly the fusion of AI and sensor technology. The goal is to propose the potential to detect and correct inaccurate or unsafe exercise postures.