EMG Signal Analysis Of Fatigue Muscle Activity In Manual Lifting

Journal of Telecommunication, Electronic and Computer Engineering, 2018

Electromyography (EMG) is one of the most commonly used tools to study human muscle condition. Past researchers have introduced various techniques from time distribution (TD), frequency distribution (FD) and timefrequency distribution (TFD) to extract information from this EMG signal. However, due to the complex characteristics of the EMG signal itself, TFD such as spectrogram has been widely used as it can provide both temporal and spectral information. However, since spectrogram has a fix window size, there exists a dilemma of resolution, where the too narrow window will result in a poor frequency resolution, and a too wide window will cause poor time resolution. Thus, this study aims to select the best window size to be used with spectrogram to monitor human muscle electrical activity during core lifting task. Four electrodes were placed over different types of muscles, which are the right and left biceps branchii (BB), and right and left erector spinae (ES). In this study, six w...

Medicine & Science in Sports & Exercise, 2002

Purpose: To test the short-term and long-term reliability of time-frequency electromyographic (EMG) measures of fatigue during repetitive dynamic lifting and compare it with the reliability of median frequency (MF) EMG measures of fatigue during static lifting. Methods: Fourteen' healthy male subjects (26 Ϯ 5 years) repetitively (12 lifts/min) lifted and lowered a box (29 ϫ 25 ϫ 23 cm, 13 kg) for 4.5 min during 3 different tests on 2 different days. EMG data and the biomechanics of motion were recorded. Before and after dynamic lifting, static maximum lifting tests were performed. At the end of each of the two sessions, subjects performed a static lift at 80% of their maximum lifting force for 30 s. Results: Significant fatigue-related changes were observed during the lifting exercise via EMG time-frequency analysis at the paravertebral L5, L2, T10, and vastus lateralis (VL) electrode sites. Two parameters for assessing fatigue during dynamic contractions [i.e., the Instantaneous Median Frequency (IMDF) and its time dependent change] were shown to be reproducible both in the short-term (2 h) and long-term (2 wk). The corresponding ICCs reflecting the reproducibility of values between sessions were 96.9% (L5), 98.1% (L2), 90.1% (T10), 96.4% (UT), 98.0% (GM), 89.5% (VL), and 99.0% (BF), respectively. For most EMG recording sites, the reliability of the IMDF measures was not dependent upon the postural strategy that the subject used to accomplish the lifting task or on the subject's strength as measured via the static maximum lifting test. A comparison between the ICC values of the IMDF measures and those of the parameters utilized to assess fatigue during static sustained lifts [i.e., the Median Frequency (MDF) and its change during the test] revealed equally good reproducibility for most EMG recording sites. The respective ICC values that took into account time dependent trends for the IMDF parameter were 87.1% (L5), 62.4% (L2), 90.1% (T10), 0% (UT), 72.7% (GM), 45.4% (VL), and 100% (BF), and for the MDF parameter 94.9% (L5), 73.0% (L2), 80.9% (T10), 100% (UT), 89% (GM), 91.7% (VL), and 90.9% (BF), respectively. Conclusions: The time-frequency approach allows one to derive EMG spectral parameters that can be used to monitor muscle fatigue during dynamic real-world tasks such as lifting.

2006

When a muscle cannot maintain the sustained contraction against a certain force level, this situation points out the onset of muscle fatigue. In many bio-mechanical studies, it is pursued to determine the fatigue by using electromyography signals, but none of them is capable of characterizing the fatigue in a quantitative manner. The need for determination of a fatigue index from the point of view of quantitative evaluation is derived from the use in physiotherapy exercises. In this study, EMG signals are recorded from biceps and triceps muscles during isometric contraction from 12 healthy subjects. Then, median frequency, and temporal and spectral moments, which are characterizing features of EMG signals are calculated. It is concluded that using higher order temporal and spectral moments for determining the muscle fatigue improves the performance compared to using only change in the median frequency.

2010

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in maximal constant load dynamic exercise (100% W<;;sub>max<;;/sub>). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in maximal constant load dynamic exercise (P&#x003E;0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P<;;0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

2012

Lower limb muscles activation was assessed during cycling to exhaustion using frequency band analysis. Nine cyclists were evaluated in two days. On the first day, cyclists performed a maximal incremental cycling exercise to measure peak power output, which was used on the second day to define the workload for a constant load time to exhaustion cycling exercise (maximal aerobic power output from day 1). Muscle activation of vastus lateralis (VL), long head of biceps femoris (BF), lateral head of gastrocnemius (GL), and tibialis anterior (TA) from the right lower limb was recorded during the time to exhaustion cycling exercise. A series of nine band-pass Butterworth digital filters was used to analyze muscle activity amplitude for each band. The overall amplitude of activation and the high and low frequency components were defined to assess the magnitude of fatigue effects on muscle activity via effect sizes. The profile of the overall muscle activation during the test was analyzed using a second order polynomial, and the variability of the overall bands was analyzed by the coefficient of variation for each muscle in each instant of the test. Substantial reduction in the high frequency components of VL and BF activation was observed. The overall and low frequency bands presented trivial to small changes for all muscles. High relationship between the second order polynomial fitting and muscle activity was found (R 2 > 0.89) for all muscles. High variability (~25%) was found for muscle activation at the four instants of the fatigue test. Changes in the spectral properties of the EMG signal were only substantial when extreme changes in fatigue state were induced.

IOP Conference Series: Materials Science and Engineering, 2019

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.

Journal of Electromyography and Kinesiology, 1997

The purpose of the current study was to determine the validity of quantifying biceps brachii fatigue with dynamic measures of surface electromyographic (EMG) mean power frequency (MPF) through comparisons with the well-established isometric methodology. Subjects performed repetitive elbow flexion<xtension movements with a hand held load of 7 kg until volitional exhaustion. Elbow joint angle and biceps brachii EMG signals were recorded continuously during the fatiguing movement (in 250-ms segments) and during isometric, isotonic contractions (in IOOO-ms segments) performed at a 90" flexion angle before and after the trial. The MPF and average EMG amplitude (AEMG) were also calculated with each sample, and a polynomial regression analysis was used to characterize the time history of changes and to determine the rested and fatigued values for the dynamic EMG with: (a) all dynamic samples above 5% MVC and (b) only samples where the elbow joint was between 80' and 100" of flexion. There was a significant increase in AEMG and a decrease in MPF for the isometric contractions and both dynamic methods. When compared to dynamic values at rest and fatigue, the isometric AEMG and MPF were substantially lower and slightly higher, respectively. No significant differences were observed between the AEMG or MPF results from the two methods of processing the dynamic EMG. The decreases in MPF ranged from 25% to 29% and did not differ between methods. The absolute and relative increases in isometric AEMG were substantially lower than with both dynamic methods. The current results support the use of MPF values from surface EMG signals recorded during dynamic contractions to quantify fatigue of the biceps brachii muscle. The proposed methodology can be used to monitor fatigue continuously throughout a dynamic movement with minimal disturbance to the task being performed and without the need to monitor joint angles. 0 1997 Elsevier Science Ltd.

Applied Mathematics, 2014

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in supramaximal constant load dynamic exercise (110% VO 2peak ). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in supramaximal constant load dynamic exercise (P>0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P<0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

… in Medicine and …, 2010

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and ...

Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2022

Physical activity is an activity of body movement by utilizing skeletal muscles that are carried out daily. One form of physical activity is an exercise which aims to improve health and fitness. Parameters related to health and wellness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. The authors used electromyographic (EMG) signals to measure muscle fatigue by monitoring changes in electrical muscle activity. This study aims to analyze the effect of muscle fatigue on cardiac signals during subjects perform physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. The authors use MPF, MDF, and MNF to get more detailed measurement results and perform a T-test. The test results showed a significant value (p-value <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628, indicating a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the device in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.