Research on Real-time Gesture Classification Algorithm Based on IMU and sEMG Mixed Signals

Authors

Tao Wang, 1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;Follow
Yingnian Wu, 1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;2.Intelligent Perception and Control of High-end Equipment Beijing International Science and Technology Cooperation Base, Beijing 100192, China;3.Intelligent Networked Things and Cooperative Control, Beijing 100101, China;Follow
Rui Yang, 1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;
Yueying Sun, 1.School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;

Abstract

Abstract: In order to improve the gesture classification accuracy of surface electromyography (sEMG), the mixed signal of attitude and sEMG is collected by inertial measurement unit (IMU) and EMG sensor, and a GRU-BiLSTM double-layer network real-time gesture classification algorithm is proposed. The first layer of gated recurrent unit (GRU) detects the mutation point of the initial mixed signal though energy combination operator feature and locates the starting point of the dynamic data. The second layer Bi-directional long short term memory (BiLSTM) classifies the motion state mixed signal into 10 gestures in two directions though energy kernel phase map feature. Through offline model optimization, the recognition time of the online GRU-BiLSTM double-layer classifier algorithm is less than 40 ms, the detection accuracy of mutation points is over 88.7%, the accuracy of gesture classification is 85%, and the information transmission rate (ITR) reaches 89.9 bits/min. Compared with machine learning-based classification algorithms, the algorithm has advantages in accuracy and computational efficiency.

Recommended Citation

Wang, Tao; Wu, Yingnian; Yang, Rui; and Sun, Yueying (2023) "Research on Real-time Gesture Classification Algorithm Based on IMU and sEMG Mixed Signals," Journal of System Simulation: Vol. 35: Iss. 2, Article 13.
DOI: 10.16182/j.issn1004731x.joss.21-1117
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/13

First Page

359

Revised Date

2021-12-24

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-1117

Last Page

371

CLC

TP249

Recommended Citation

Tao Wang, Yingnian Wu, Rui Yang, Yueying Sun. Research on Real-time Gesture Classification Algorithm Based on IMU and sEMG Mixed Signals[J]. Journal of System Simulation, 2023, 35(2): 359-371.

Corresponding Author

Yingnian Wu,wuyingnian@126.com

DOI

10.16182/j.issn1004731x.joss.21-1117