A comparison of sEMG and MMG signal classification for automated muscle fatigue detection

Autor:	Mohammed R. Al-Mulla, Francisco Sepulveda
Rok vydání:	2019
Předmět:	Dynamic contractions Muscle fatigue medicine.diagnostic_test business.industry Feature extraction Biomedical Engineering Percentage point Pattern recognition Electromyography Accelerometer Biceps Wavelet medicine Artificial intelligence business Mathematics
Zdroj:	International Journal of Biomedical Engineering and Technology. 30:277
ISSN:	1752-6426 1752-6418
Popis:	This study compares the classification performance of both sEMG and MMG signal from fatiguing dynamic contraction of the biceps brachii. Commonly used statistical features are compared with a recently developed evolved pseudo-wavelet. Based on the literature, wavelet-based methods are a promising feature extraction technique for both types of signals (sEMG and MMG) during dynamic contractions. MMG results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 27.94 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05). For sEMG signals the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.96 percentage points when compared to other standard wavelet functions (p < 0.05), giving an average correct classification of 87.90%. The comparison demonstrates that for both the sEMG and the MMG signal, the feature giving best classification results was the evolved pseudo-wavelet.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75e2194977693b410b16ee2e385ef389 https://doi.org/10.1504/ijbet.2019.10022269 Zobrazit plný text záznamu