/pdf/recent-advances-and-challenges-in-textile-electrodes-for-3efxcygs.pdf

Recent Advances and Challenges in Textile Electrodes for Wearable Biopotential Signal Monitoring: A Comprehensive Review

Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.

/pdf/heart-rate-variability-based-subjective-physical-fatigue-1a8dghgq.pdf

Heart Rate Variability-Based Subjective Physical Fatigue Assessment

Complexity-based analysis of the correlation between stride interval variability and muscle reaction at different walking speeds

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the al...

Analysis of the correlation between muscle reaction and stride interval variability in single-task and dual-task walking

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features.

Muscle fatigue is described by the decline in muscle maximum force during contraction. The fatigue occurs in the nervous or muscle fibre cells. The nerves produce a high-frequency signal to gain the maximum contraction, but it cannot sustain the high frequency signal for a long time, and that leads to a decline in muscle force. The surface Electromyography (EMG) is the dominant method to detect muscle fatigue because the EMG signals give more information about the muscle's activities. This review discussed the EMG signal processing and the methods of detection muscles fatigue with three domains (time domain, frequency domain, and time-frequency domain) based on EMG signals that are collected from the muscles during dynamic and static movements.

https://iopscience.iop.org/article/10.1088/1757-899X/705/1/012010/pdf

Assessment of Muscles Fatigue Based on Surface EMG Signals Using Machine Learning and Statistical Approaches: A Review

Incorrect running may lead to discomfort and injuries, where each day around the world, the numbers of runners are increasing. The goal of this research work is to evaluate and study the lower limb muscles fatigue during running for 400-meters with two types of running strategies based on the Electromyography (EMG) signals. The EMG signals are collected from Rectus Femoris (RF), Biceps Femoris (BF), and Gastrocnemius Lateralis (GL) muscles during the run on the tartan athletic track with two types of running strategies. The first type: the first 200-meters running with normal speed and the last 200-meters running with full speed. The second type: the first 300-meters running with normal speed and the last 100-meters running with full speed. The EMG signals were transformed into the frequency domain using fast Fourier transform (FFT) to extract the features of mean frequency (MNF) and median frequency (MDF). From the results of the two strategies with MDF and MNF features of the selected muscles, the lowest fatigue index was during the 1st strategy for most the selected muscles.

A Study of Lower Limb Muscles Fatigue during Running Based on EMG Signals

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Assessment of Muscles Fatigue Based on Surface EMG Signals Using Machine Learning and Statistical Approaches: A Review

A Study of Lower Limb Muscles Fatigue during Running Based on EMG Signals