Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

Shili Liang,Yansheng Wu,Jipeng Huang,Jianfei Chen,Shifeng Yan

doi:10.1109/access.2020.3021072

Shili Liang, Yansheng Wu + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3021072

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Abstract

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

Highlights

Surface electromyography signal comprehensively reflects the muscle movement patterns on the surface of the human body through the velocity information conducted by the activated nerve potential of motor nerves [1], which can characterize the ongoing limb movement, and reflect the movement intention that has not been converted into the actual movement, such as the extending and flexing conditions of the joints, and the shape and position of the limbs for the human body, and transmits the movement intention of the limbs to the brain
EXPERIMENTAL PROCESS This paper proposes a recognition system for subtle gestures based on two-channel Surface electromyography (sEMG) signals using the parameter estimation classifiers based on probability density
It can be seen from Fig.c1 and Fig.d1 that there is a significant baseline drift at the tail of the waveform before filtering, and the waveform has a lot of glitches during the relaxation phase

Summary

Introduction

Surface electromyography (sEMG) signal comprehensively reflects the muscle movement patterns on the surface of the human body through the velocity information conducted by the activated nerve potential of motor nerves [1], which can characterize the ongoing limb movement, and reflect the movement intention that has not been converted into the actual movement, such as the extending and flexing conditions of the joints, and the shape and position of the limbs for the human body, and transmits the movement intention of the limbs to the brain. In order to reduce the environmental interference of the experiment and improve the accuracy of effective motion extraction, some researchers prefer to use the developed wired equipment to collect sEMG signal data. The classifiers QDA and LDA based on the two-channel sEMG signal data are used to realize the recognition of 9 kinds of subtle gestures, including multi-finger pinching modes such as clicking between fingers and pinching objects.

Results

Conclusion