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A muscle synergies-based movements detection approach for recognition of the wrist movements
- Source :
- EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-19 (2020)
- Publication Year :
- 2020
- Publisher :
- SpringerOpen, 2020.
-
Abstract
- Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.
- Subjects :
- Wrist movement
Computer science
0206 medical engineering
Prosthetic limb
lcsh:TK7800-8360
02 engineering and technology
Electromyography
Wrist
Muscle synergy
lcsh:Telecommunication
lcsh:TK5101-6720
0202 electrical engineering, electronic engineering, information engineering
medicine
Reliability (statistics)
Artificial neural network
medicine.diagnostic_test
business.industry
lcsh:Electronics
Pattern recognition
Perceptron
020601 biomedical engineering
Electromyogram
medicine.anatomical_structure
020201 artificial intelligence & image processing
Decision fusion
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 16876180
- Volume :
- 2020
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- EURASIP Journal on Advances in Signal Processing
- Accession number :
- edsair.doi.dedup.....4400567ec90cc05c39c93357f2c36ecb
- Full Text :
- https://doi.org/10.1186/s13634-020-00699-y