Combining Virtual Reality Technology With Biofeedback System to Optimize the Skill Mastery and Teaching Effect of Massage Trainees

Abstract

Abstract. Traditional massage teaching lacks quantifiable data and real-time feedback, making it hard for trainees to master proper technique. Existing virtual systems focus on visual simulation but neglect mechanical and physiological feedback. This paper integrates multimodal biofeedback systems, including electromyography (sEMG), pressure, and posture, with a virtual interactive platform to create a “visible-touchable-evaluable” closed-loop teaching system. The system uses hand training gloves equipped with force sensing resistor (FSR), inertial measurement unit (IMU), and sEMG sensors to collect real-time data on force and muscle group activation. Long short-term memory (LSTM) identifies temporal movement patterns and detects deviations. A teacher-side interface provides visual feedback on force, trajectory, and rhythm. Experimental results show that the average trajectory deviation reduces from 18.31% to 15.22%, with force uniformity improving and muscle activation increasing from 0.636 to 0.844. This method significantly enhances massage teaching quality and trainee skill mastery.