Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods
| Autor: | Andrzej Wolczowski, Rafal Zdunek |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computational complexity theory business.industry Dimensionality reduction Biomedical Engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology Electromyography Signal Non-negative matrix factorization Singular value decomposition Principal component analysis 0202 electrical engineering electronic engineering information engineering medicine Spectrogram 020201 artificial intelligence & image processing Artificial intelligence business Mathematics |
| Zdroj: | Biocybernetics and Biomedical Engineering. 37:103-113 |
| ISSN: | 0208-5216 |
| DOI: | 10.1016/j.bbe.2016.09.004 |
| Popis: | In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated. |
| Databáze: | OpenAIRE |
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