Next Article in Journal
Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior
Next Article in Special Issue
Bionic Design of a Novel Portable Hand-Elbow Coordinate Exoskeleton for Activities of Daily Living
Previous Article in Journal
DHD-MEPO: A Novel Distributed Coverage Hole Detection and Repair Method for Three-Dimensional Hybrid Wireless Sensor Networks
Previous Article in Special Issue
Stability Study of an Interventional Surgery Robot Based on Active Disturbance Rejection Control
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm

1
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
2
Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
3
Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, China
*
Author to whom correspondence should be addressed.
Electronics 2023, 12(11), 2444; https://doi.org/10.3390/electronics12112444
Submission received: 24 February 2023 / Revised: 18 May 2023 / Accepted: 22 May 2023 / Published: 28 May 2023
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)

Abstract

Currently, sEMG-based pattern recognition is a crucial and promising control method for prosthetic limbs. A 1D convolutional recurrent neural network classification model for recognizing online finger and wrist movements in real time was proposed to address the issue that the classification recognition rate and time delay cannot be considered simultaneously. This model could effectively combine the advantages of the convolutional neural network and recurrent neural network. Offline experiments were used to verify the recognition performance of 20 movements, and a comparative analysis was conducted with CNN and LSTM classification models. Online experiments via the self-developed sEMG signal pattern recognition system were established to examine real-time recognition performance and time delay. Experiment results demonstrated that the average recognition accuracy of the 1D-CNN-RNN classification model achieved 98.96% in offline recognition, which is significantly higher than that of the CNN and LSTM (85.43% and 96.88%, respectively, p < 0.01). In the online experiments, the average accuracy of the real-time recognition of the 1D-CNN-RNN reaches 91% ± 5%, and the average delay reaches 153 ms. The proposed 1D-CNN-RNN classification model illustrates higher performances in real-time recognition accuracy and shorter time delay with no obvious sense of delay in the human body, which is expected to be an efficient control for dexterous prostheses.
Keywords: 1D-CNN; RNN; surface EMG; pattern recognition; real-time identification 1D-CNN; RNN; surface EMG; pattern recognition; real-time identification

Share and Cite

MDPI and ACS Style

Li, S.; Zhang, Y.; Tang, Y.; Li, W.; Sun, W.; Yu, H. Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm. Electronics 2023, 12, 2444. https://doi.org/10.3390/electronics12112444

AMA Style

Li S, Zhang Y, Tang Y, Li W, Sun W, Yu H. Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm. Electronics. 2023; 12(11):2444. https://doi.org/10.3390/electronics12112444

Chicago/Turabian Style

Li, Sujiao, Yue Zhang, Yuanmin Tang, Wei Li, Wanjing Sun, and Hongliu Yu. 2023. "Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm" Electronics 12, no. 11: 2444. https://doi.org/10.3390/electronics12112444

APA Style

Li, S., Zhang, Y., Tang, Y., Li, W., Sun, W., & Yu, H. (2023). Real-Time sEMG Pattern Recognition of Multiple-Mode Movements for Artificial Limbs Based on CNN-RNN Algorithm. Electronics, 12(11), 2444. https://doi.org/10.3390/electronics12112444

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop