Design and Research of a Wearable Portable Electromyography Monitoring System

To address the issues of complexity in operation, limited application scenarios, and inaccurate recognition in existing EMG testing systems, this paper presents the design and research of a wearable and portable EMG monitoring system suitable. The study uses Ag/AgCl patch electrodes to collect surface EMG (sEMG) signals at a frequency of 500 Hz. After preprocessing through linear interpolation, band-pass filtering, and notch filtering, features such as MAV, RMS, and iEMG are extracted. The LDA algorithm is used for gesture classification, and a user-friendly interface was developed using PyQt in PyCharm to enhance operational convenience and interaction efficiency. Experimental results show that the EMG signals collected by the system are of high quality, the algorithm performs well in recognizing different gestures, and it has excellent noise reduction capabilities. The system can continuously operate for about 8 hours, offering advantages of low cost, small size, easy operation, and stable performance, providing a theoretical foundation for portable personal EMG monitoring.