Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory

OBJECTIVE

Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robot. However, the existing methods mostly use the signal segmentation processing way rather than point-to-point signal processing way and lack physiological mechanism support.

APPROACH

In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, a sEMG signal feature extraction method is constructed and ensemble learning method is combined to achieve the real-time human hand motion intention recognition.

MAIN RESULTS

The experimental results show that the average root mean square difference (RMSD) value of sEMG signal modeling is 0.3838±0.0591, and the average accuracy of human hand motion intention recognition is 96.03±1.74%. On a computer with an Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 second is 0.66 second.

SIGNIFICANCE

Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective of sEMG modeling and feature extraction for hand movement classification.

ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN

This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG signal. This work presents a global EMG index map by using the new analytical technique based on the relationship between the average IEMG and mean power frequency (MPF) values. The proposed method enables simultaneous estimation of muscle fatigue level and force using real-time EMG signals from the femoral and gastrocnemius muscles.