Muscle fatigue analysis in young adults at different MVC levels using EMG metrics

Zaman, A. Al; Sharmin, T.; Khan, Mohammad Ashraf; Ferdjallah, M.

doi:10.1109/secon.2007.342930

Cited by 10 publications

(14 citation statements)

References 16 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%

“…where T is the length of the interval where the SEMG signal s(t) is analyzed [15], [19], [32] [9]. Another technique in the literature regarding the estimation of spectral variables (MNF, MF, and amplitude) is the use of time-varying autoregressive models (TVAR).…”

Section: Overviewmentioning

confidence: 99%

“…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]. 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.…”

Section: Introductionmentioning

confidence: 99%

“…Time-frequency (TF) distributions from the Cohen class and wavelets have demonstrated to be useful approaches in the analysis of SEMG signals with static and dynamic muscle contractions [8], [12], [25], [11], [4], [13], [3]. The spectrogram is an example of TF distributions that has been used in the analysis of static and dynamic muscle contractions with low to medium non-stationarities [32], [9]. The objective of TF distributions and wavelets in this context is to provide information about how the energy of the signal is distributed over time and frequency.…”

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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