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Sensors 2015, 15(4), 9022-9038; doi:10.3390/s150409022

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

1
The Institute of Advanced Biomedical Engineering System, School of Life Science and Technology, Beijing Institute of Technology, Haidian District, Beijing 100081, China
2
Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science and Technology, Beijing Institute of Technology, Haidian District, Beijing 100081, China
3
Faculty of Engineering, Kagawa University, Hayashi-cho, Takamatsu 761-0369, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Oliver Amft
Received: 23 December 2014 / Revised: 8 April 2015 / Accepted: 10 April 2015 / Published: 16 April 2015
(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)
View Full-Text   |   Download PDF [1134 KB, uploaded 16 April 2015]   |  

Abstract

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement. View Full-Text
Keywords: surface electromyography; motion recognition; muscular model; weight peaks; neural networks; support vector machine surface electromyography; motion recognition; muscular model; weight peaks; neural networks; support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guo, S.; Pang, M.; Gao, B.; Hirata, H.; Ishihara, H. Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement. Sensors 2015, 15, 9022-9038.

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