Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy

J. Murillo-Escobar; Y. E. Jaramillo-Munera; D. A. Orrego-Metaute; E. Delgado-Trejos; D. Cuesta-Frau; J. Murillo-Escobar; Y. E. Jaramillo-Munera; D. A. Orrego-Metaute; E. Delgado-Trejos; D. Cuesta-Frau

doi:10.3934/mbe.2020142

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 3: 2592-2615. doi: 10.3934/mbe.2020142

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Research article Special Issues

Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy

1.
Department of Exact and Applied Sciences, GI2B Research Group, Instituto Tecnologico Metropolitano ITM, CL 73 No. 76 A 354, Medellin, Colombia
2.
AMYSOD Lab –Parque i, CM&P Research Group, Instituto Tecnologico Metropolitano ITM, CL 73 No. 76 A 354, Medellin, Colombia
3.
Technological Institute of Informatics, Universitat Politecnica de Valencia, Alcoi Campus, 03801 Alcoi, Spain

Received: 28 September 2019 Accepted: 19 December 2019 Published: 04 March 2020

Muscle fatigue is an important field of study in sports medicine and occupational health. Several studies in the literature have proposed methods for predicting muscle fatigue in isometric con-tractions using three states of muscular fatigue: Non-Fatigue, Transition-to-Fatigue, and Fatigue. For this, several features in time, spectral and time-frequency domains have been used, with good performance results; however, when they are applied to dynamic contractions the performance decreases. In this paper, we propose an approach for analyzing muscle fatigue during dynamic contractions based on time and spectral domain features, Permutation Entropy (PE) and biomechanical features. We established a protocol for fatiguing the deltoid muscle and acquiring surface electromiography (sEMG) and biomechanical signals. Subsequently, we segmented the sEMG and biomechanical signals of every contraction. In order to label the contraction, we computed some features from biomechanical signals and evaluated their correlation with fatigue progression, and the most correlated variables were used to label the contraction using hierarchical clustering with Wardos linkage. Finally, we analyzed the discriminant capacity of sEMG features using ANOVA and ROC analysis. Our results show that the biomechanical features obtained from angle and angular velocity are related to fatigue progression, the analysis of sEMG signals shows that PE could distinguish Non-Fatigue, Transition-to-Fatigue and Fatigue more effectively than classical sEMG features of muscle fatigue such as Median Frequency.
- Muscle fatigue,
- unsupervised learning,
- biomechanics,
- Hierarchical clustering,
- sEMG,
- Permutation Entropy
Citation: J. Murillo-Escobar, Y. E. Jaramillo-Munera, D. A. Orrego-Metaute, E. Delgado-Trejos, D. Cuesta-Frau. Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2592-2615. doi: 10.3934/mbe.2020142

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Abstract

Muscle fatigue is an important field of study in sports medicine and occupational health. Several studies in the literature have proposed methods for predicting muscle fatigue in isometric con-tractions using three states of muscular fatigue: Non-Fatigue, Transition-to-Fatigue, and Fatigue. For this, several features in time, spectral and time-frequency domains have been used, with good performance results; however, when they are applied to dynamic contractions the performance decreases. In this paper, we propose an approach for analyzing muscle fatigue during dynamic contractions based on time and spectral domain features, Permutation Entropy (PE) and biomechanical features. We established a protocol for fatiguing the deltoid muscle and acquiring surface electromiography (sEMG) and biomechanical signals. Subsequently, we segmented the sEMG and biomechanical signals of every contraction. In order to label the contraction, we computed some features from biomechanical signals and evaluated their correlation with fatigue progression, and the most correlated variables were used to label the contraction using hierarchical clustering with Wardos linkage. Finally, we analyzed the discriminant capacity of sEMG features using ANOVA and ROC analysis. Our results show that the biomechanical features obtained from angle and angular velocity are related to fatigue progression, the analysis of sEMG signals shows that PE could distinguish Non-Fatigue, Transition-to-Fatigue and Fatigue more effectively than classical sEMG features of muscle fatigue such as Median Frequency.

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