Electromyographical indication of muscular fatigue in occupational field studies

Luttmann, Alwin; Jäger, Matthias; Laurig, Wolfgang

doi:10.1016/s0169-8141(99)00053-0

Cited by 149 publications

(139 citation statements)

References 26 publications

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“…Rather, others state that the MNF is more stable and sensitive to changes in the underlying spectrum [20]. Also, it has been proposed that in the assessment of muscle fatigue one should analyze changes in both the power spectrum and the amplitude of the SEMG signal [15], [20], [16], [19], [32] [9]. In the literature different techniques are found to estimate spectral variables (MNF, MF and IF) and the amplitude of the SEMG signal.…”

Section: Overviewmentioning

confidence: 99%

“…In the literature different techniques are found to estimate spectral variables (MNF, MF and IF) and the amplitude of the SEMG signal. A classical method for estimating the MNF and the MF is explained in [15], [20], [19], [32], [9]. First, the SEMG signal is divided into equal segments (epochs) with a duration of 500 ms or 1 s. If the signal is non-stationary, then the SEMG signal is divided into epochs of 250 ms or 500 ms [9].…”

Section: Overviewmentioning

confidence: 99%

“…Specifically, muscle fatigue involves a decrease in the maximum force and a measurable shift in the power spectrum of the SEMG signal, which is commonly reported to occur toward lower frequencies [15], [20], [16], although there are fewer reports of increased frequencies [16]. Consequently, the mean frequency, median frequency, and IF have been extensively used in the assessment of muscle fatigue as indicators of how the frequency of the SEMG signal changes over time [20], [25], [30], [19]. Additionally, muscle fatigue also involves an increase in the SEMG signal's amplitude [15], [20], [32], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, muscle fatigue also involves an increase in the SEMG signal's amplitude [15], [20], [32], [9]. The root mean square, the average rectified value, and a recent technique introduced by Merletti [21], [15], [19], [27] have been proposed as amplitude estimators of the SEMG signal, that can then be used as an additional input to the analysis of muscle fatigue. When considering changes in the frequency and amplitude of the SEMG signal to investigate muscle fatigue, the level of stationarity of the signal should be considered.…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, they have been used to estimate the frequency but not the amplitude. An exception is the work by Luttman [20], [19], which introduces a joint analysis of spectrum and amplitude (JASA) of SEMG signals. During this research, JASA is referred as a joint analysis of frequency and amplitude.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Potes¹,

Borries²,

Miosso³

2008

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MMM-YYYY) June 2008 REPORT TYPE Final Report DATES COVERED (From -To)September 2006 SPONSOR / MONITOR'S ACRONYMAFRL/RHPA SPONSOR/MONITOR'S REPORT NUMBER(S) AFRL-RH-WP-TR-2008-0066 DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared 4Aug08, WPAFB-08-4987. ABSTRACTMuscle fatigue involves both a decrease in the frequency and increase in the amplitude of a surface electromyographic (SEMG) signal. Muscle fatigue is also related to a decrease of the force impeding to reach the same initial level of the maximum voluntary contraction (MVC). To determine indices of muscle fatigue, a method is proposed to estimate both the instantaneous frequency (IF) and the instantaneous amplitude (IA) by decomposing the SEMG signal using a filter bank. A linear regression model was adopted to compute the IF and IA slopes. These slopes were then classified in muscle increase force, recovery, muscle decrease force and fatigue by using a joint analysis of frequency and amplitude. SEMG signals were recorded from 26 normal human subjects when doing an exertion of 70% and 100% of their MVC during a session of eight hours. It was found that slopes derived from the proposed filter bank are equivalent to those slopes derived from the spectrogram and the smoothed pseudo Wigner-Ville distribution. Furthermore, slopes derived from the filter bank indicated that they can be used as indices to determine muscle fatigue. These results were confirmed by correlating indices of muscle fatigue with perceived levels of discomfort reported by the subjects after performing an exertion of 70% MVC in hours two, four, and six.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Potes¹,

Borries²,

Miosso³

2008

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Surface EMG in Ergonomics and Occupational Medicine

Gazzoni

Afsharipour

Merletti

2016

Surface Electromyography : Physiology, Engineering, and Applications

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A Flexible Wireless sEMG System for Wearable Muscle Strength and Fatigue Monitoring in Real Time

Gong

Jiang

Liu

et al. 2023

Adv Elect Materials

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The detection of surface electromyography (sEMG) signals on the skin has attracted increasing attention because of its ability to monitor muscle conditions in a noninvasive manner and thus possesses great application potential to assess athletic status and training efficiency in real time or to evaluate postoperative muscle rehabilitation conveniently. Here, a flexible wireless sEMG monitoring system that consists of a stretchable sEMG epidermal patch and a flexible printed circuit board to provide real‐time evaluation of muscle strength and fatigue is reported. The epidermal patch is designed to have good stretchability and permeability and optimized to ensure a low contact impedance with the skin and minimized background noise for sEMG signal acquisition with high fidelity. Six commonly used time‐domain and two frequency‐domain features extracted from sEMG signals are systematically analyzed, and a strategy for feature selection and pattern identification is proposed that eventually enables the real‐time assessment of muscle strength and fatigue by using an integrated system in a wearable form.

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Electromyographical indication of muscular fatigue in occupational field studies

Cited by 149 publications

References 26 publications

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Wavelet-Based Signal Processing for Monitoring Discomfort and Fatigue

Surface EMG in Ergonomics and Occupational Medicine

A Flexible Wireless sEMG System for Wearable Muscle Strength and Fatigue Monitoring in Real Time

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