The reliability of determining the onset of medial gastrocnemius muscle activity during a stretch-shorten-cycle action

Gupta, Amitabh; Mudie, Kurt; Clothier, Peter J.

doi:10.1016/j.jelekin.2014.05.005

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Hodges and Bui 11 suggest that the onset and offset of muscle activity can be calculated or visually determined by analyzing the sEMG of upper limbs movements. Gupta et al 17 compared the visual analysis method with the method calculated by software to determine the duration of medial gastrocnemius muscle activity during the jump and concluded that the visual method could generate a better representation of the time characteristics of the onset of muscle activity. The determination of the onset of contraction visually was also suggested for women with PFM dysfunction in the study of Koenig et al 8 The agreement for evaluating the quality of the sEMG signal was fair, which can be explained by the chosen evaluation method.…”

Section: Discussionmentioning

confidence: 99%

Electromyographic analysis of maximal voluntary contraction of female pelvic floor muscles: Intrarater and interrater reliability study

Volpato

Menezes

Prado

et al. 2021

Neurourology and Urodynamics

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Aims: To investigate the intrarater and interrater reliability of the surface electromyography (sEMG) of female pelvic floor muscles (PFM) by visual determination of the onset-offset during maximal voluntary contraction (MVC).Methods: Intrarater and interrater reliability study. Three MVC, recorded for 5 s (s) each and separated by intervals of 60 s, were requested. Two independent raters visually determined the onset and offset of each MVC. The agreement between raters on the quality of the signals was analyzed both qualitatively (visual inspection) and quantitatively (signal-to-noise ratio, SNR).The reliability of integral and median frequency (MDF) of sEMG signals during MVC was analyzed using intraclass correlation coefficient (ICC) models (ICC 2,1 , ICC 2,3 ) and repeated measures analysis of variance. Results: A total of 58 women (mean age of 54 years) and two independent raters participated in the analyses. Of the total, 22 (37.9%) were considered of high quality by both raters and the SNR ranged 34.3-22.2 decibels (dB). The agreement of qualitative visual evaluation of the sEMG signal was fair (Cohen's κ = 0.306 [0.148; 0.463]). Intrarater and interrater reliability were excellent for the average of the three MVC, both for integral (ICC 2,3 = 0.884 [0.821; 0.925]; ω 2 = −0.006, p = 0.558) and MDF (ICC 2,3 = 0.998 [0.998; 0.999]; ω 2 = −0.009, p = 0.992). Conclusion:Qualitative visual analysis of the sEMG signal presents fair interrater agreement and reflects the SNR. Visual determination of the onset and offset of the MVC of PFM using the standard interface of the sEMG software has excellent reliability for determination of muscle activation variables.

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Section: Discussionmentioning

confidence: 99%

Electromyographic analysis of maximal voluntary contraction of female pelvic floor muscles: Intrarater and interrater reliability study

Volpato

Menezes

Prado

et al. 2021

Neurourology and Urodynamics

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“…Other methods used for preprocessing include Independent Component Analysis (ICA) and empirical mode decomposition (EMD) [104], Ensemble Empirical Mode Decomposition (EEMD) with Hilbert Transform (HT) [105], and Discrete Wavelet Transform (DWT) [106]. In estimating muscle activity onsets, methods such as visual and automated methods [107], sample entropy (SampEn) analysis [108], and sequential Gaussian mixture model (GMM) have been proposed [109]. Regarding feature extraction, four main types of features are extracted from sEMG signals time-domain, frequency-domain, timefrequency domain, and nonlinear parameters [110][111][112].…”

Section: Methods For Connecting Sensors To Intelligent Garment Systemmentioning

confidence: 99%

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Shen,

Tao,

Koncar

et al. 2023

IEEE Access

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Bioelectric signals are significant indicators of the state of health of the human body, especially in sports monitoring, where athletes' fatigue state and performance need to be monitored in realtime to develop a proper training plan. Due to the characteristics of sports, it is difficult to obtain the dynamic bioelectrical signals of the human body during exercise. This paper provides a comprehensive overview of the current knowledge on Intelligent Garment Systems (IGS) for long-lasting bioelectric monitoring in sports. This review includes a detailed examination of human bioelectric signals, focusing on ECG, EMG, and GSR signals and their applications in intelligent wearable technologies. The definition and development history of IGS is also discussed, along with a review of the primary research components of IGS, including dry textile electrodes, methods for connecting sensors to IGS, and processing methods for bioelectric signals. The paper concludes by highlighting the current challenges faced by IGS in terms of real-time dynamic monitoring and connection problems and outlining the future directions for this field, including the need for further advancements in bioelectric signal processing and analysis, the development of new materials and connection technologies, and the integration of artificial intelligence and machine learning into IGS.

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“…Muscle activity onset can be estimated from EMG and ultrasound ( Dieterich et al, 2017 ). Gupta et al (2014) determined the onset of medial gastrocnemius muscle activity using visual and automated methods during a stretch-shorten-cycle muscle action. Zhang and Zhou (2012) proposed a novel method of muscle activity onset detection based primarily on the sample entropy (SampEn) analysis of the surface EMG.…”

Section: Acquisition Of Surface Electromyography and Preprocessingmentioning

confidence: 99%

Application of Surface Electromyography in Exercise Fatigue: A Review

Sun

Liu

Lin

et al. 2022

Front. Syst. Neurosci.

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Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

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The reliability of determining the onset of medial gastrocnemius muscle activity during a stretch-shorten-cycle action

Cited by 10 publications

References 28 publications

Electromyographic analysis of maximal voluntary contraction of female pelvic floor muscles: Intrarater and interrater reliability study

Electromyographic analysis of maximal voluntary contraction of female pelvic floor muscles: Intrarater and interrater reliability study

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Application of Surface Electromyography in Exercise Fatigue: A Review

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