Application of Surface Electromyography in Exercise Fatigue: A Review

Sun, Jiaqi; Liu, Guangda; Lin, Kai; Zhou, Zijian; Cai, Jing

doi:10.3389/fnsys.2022.893275

Cited by 28 publications

(12 citation statements)

References 98 publications

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“…Then, 200-ms data windows were extracted with an increment step of 50 ms. A total of nine representative features were extracted from the data windows. Six of which were the typical Hudgins time-domain features, including root mean square (RMS), mean absolute value (MAV), integrated EMG (iEMG), waveform length (WL), zero crossing (ZC), and slope sign change (SSC) [ 42 ]. These features are widely used in sEMG signal analysis due to their computational simplicity and effectiveness in extracting time-domain information.…”

Section: Methodsmentioning

confidence: 99%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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Background In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. Methods A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. Results Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. Conclusion This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

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Section: Methodsmentioning

confidence: 99%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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“…Quantitative assessment of muscle fatigue using surface electromyography (sEMG) has been the subject of numerous studies ( Zhang et al, 2019 ; Yun et al, 2020a ; Yun et al, 2020b ) leading to the need to develop electromyographic models that can correlate changes in sEMG signals with muscle fatigue ( González-Izal et al, 2012 ). Evaluation of muscle fatigue based on EMG signals can be performed using various methods, including spectral analysis, time-frequency distribution analysis, fractal analysis, and signal entropy analysis ( González-Izal et al, 2012 ; Sun et al, 2022 ). One of the most popular methods for quantitatively determining muscle fatigue based on non-invasive sEMG measurements is spectral analysis, specifically analyzing the frequency parameters: mean frequency (MNF) and median frequency (MDF) of the electromyographic signal ( Phinyomark et al, 2012a ; Kim et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG)

Daniel,

Małachowski,

Sybilski

et al. 2024

Front. Bioeng. Biotechnol.

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In this paper, we present a quantitative assessment of muscle fatigue using surface electromyography (sEMG), a widely recognized method that is conducted through various analytical approaches, including analysis of spectral and time-frequency distributions. Existing research in this field has demonstrated considerable variability in the computational methods used. Although some studies highlight the efficacy of wavelet analysis in dynamic motion, few offer a comprehensive method for determining fatigue and applying it to specific movements. Previous research has focused primarily on discerning differences based on sport type or gender, with a notable absence of studies that presented results for quantifying fatigue during exercise with rowing ergometers. Developing on our previous work, where we introduced a method for determining muscle fatigue through wavelet analysis, considering biomechanical aspects of limb position changes, this current article serves as a continuation. Our study refines the research approach for a selected group, focusing on fatigue determination using the previously established method. The results obtained confirm the effectiveness of DWT analysis in assessing muscle fatigue, as evidenced by the achievement of negative values of the regression coefficients of Median Frequency (MDF) during exercises performed to maximal fatigue. Furthermore, it has been confirmed that the homogeneity of the group and, in the case of the examined group, the results previously achieved or lower limb strength do not have an impact on the results. Finally, we discuss the main limitations of our study and outline the subsequent steps of our investigation, providing valuable information for future investigations in this field.

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“…Real-time monitoring of the fatigue affecting a particular muscle while performing an activity is possible via surface electromyography (sEMG) [21]. Indeed, the biochemical changes in the muscles (accumulation of catabolites, such as inorganic phosphate and phosphocreatine) during strenuous contractions are also reflected in the properties of the myoelectric signal generated.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Verbal Encouragement on Performance and Muscle Fatigue in Swimming

et al. 2022

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Background and Objectives: Verbal encouragement (VE) can be used to enhance performance in several sports, even though no studies have been conducted among swimmers and only a few effects have been reported in elite athletes. Besides influencing motor performance, VE is also known to enhance the physical load, thus potentially increasing the probability of developing fatigue. With this in mind, this study aimed to explore the effects of VE in swimmers in order to fill in the knowledge gap concerning the aquatic environment. Materials and Methods: Each athlete swam a maximal 200 m freestyle trial under two different conditions: one trial with VE and the other without VE. The two main outcome measures were: (1) performance velocity (m/s); and (2) muscle fatigue, investigated by means of surface electromyography. Sixty swimmers were recruited, aged 18.63 ± 3.46 years (median 18 years), 28 men (47%), and 32 women (53%), with 7.03 ± 3.9 years of experience. Results: With VE, performance significantly improved in the swim trial (p < 0.001, effect size (ES) −0.95, large). When breaking the results down into the first half (first (0–100 m) vs. the second half (100–200 m)), the ES was large in the first part (−1.11), indicating an improvement in performance. This worsened, however, in the second part of the trial (ES 0.63). In the multivariate analysis, years of experience were found to be a significant predictor of the change in overall performance (p = 0.011). There was a significant increase in muscle fatigue induced by VE, overall, and during the second half, but not during the first half of the trial. Conclusions: The present study indicates that VE during a middle-distance event (200 m) increases performance most in swimmers with little experience. However, it has a negative impact on fatigue.

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Application of Surface Electromyography in Exercise Fatigue: A Review

Cited by 28 publications

References 98 publications

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG)

The Effect of Verbal Encouragement on Performance and Muscle Fatigue in Swimming

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