Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition
Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion in...
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13332 |
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| author | Wensheng Chen Yinxi Niu Zhenhua Gan Baoping Xiong Shan Huang |
| author_facet | Wensheng Chen Yinxi Niu Zhenhua Gan Baoping Xiong Shan Huang |
| author_sort | Wensheng Chen |
| collection | DOAJ |
| description | Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features. |
| first_indexed | 2024-03-08T21:01:33Z |
| format | Article |
| id | doaj.art-61b426d909a8425d97c3182960cfd6ee |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:33Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-61b426d909a8425d97c3182960cfd6ee2023-12-22T13:52:17ZengMDPI AGApplied Sciences2076-34172023-12-0113241333210.3390/app132413332Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture RecognitionWensheng Chen0Yinxi Niu1Zhenhua Gan2Baoping Xiong3Shan Huang4School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350116, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350116, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350116, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350116, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350116, ChinaEnhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features.https://www.mdpi.com/2076-3417/13/24/13332EMGmulti-feature fusionspatial feature information |
| spellingShingle | Wensheng Chen Yinxi Niu Zhenhua Gan Baoping Xiong Shan Huang Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition Applied Sciences EMG multi-feature fusion spatial feature information |
| title | Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition |
| title_full | Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition |
| title_fullStr | Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition |
| title_full_unstemmed | Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition |
| title_short | Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition |
| title_sort | spatial feature integration in multidimensional electromyography analysis for hand gesture recognition |
| topic | EMG multi-feature fusion spatial feature information |
| url | https://www.mdpi.com/2076-3417/13/24/13332 |
| work_keys_str_mv | AT wenshengchen spatialfeatureintegrationinmultidimensionalelectromyographyanalysisforhandgesturerecognition AT yinxiniu spatialfeatureintegrationinmultidimensionalelectromyographyanalysisforhandgesturerecognition AT zhenhuagan spatialfeatureintegrationinmultidimensionalelectromyographyanalysisforhandgesturerecognition AT baopingxiong spatialfeatureintegrationinmultidimensionalelectromyographyanalysisforhandgesturerecognition AT shanhuang spatialfeatureintegrationinmultidimensionalelectromyographyanalysisforhandgesturerecognition |