Detection of Muscle Fatigue by the Surface Electromyogram and Its Application

“…However, the objective detection of muscle fatigue requires the application of a specific type of measurement to detect the fatigue, which, in the case of this study, involves an analysis of the sEMG signals [7]. Consequently, the objective detection of muscle fatigue disregards any subjective reports by individuals [7], and, furthermore, it identifies the level of exhaustion of the muscle [8]. Hence, the objective detection of muscle fatigue is able to prevent post-fatigue injuries caused by over-training by athletes and rehabilitation exercises for stroke patients [9,10].…”

Section: Introductionmentioning

confidence: 99%

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

Qassim

Hasan

Ramli

et al. 2022

Sensors

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The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.

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“…According to the results of a previous study, a sliding from the high frequency spectrum to low frequency spectrum was observed when the muscle is fatigued [14]. Soo et al were tried to predict the amount of fatigue during the static muscle contraction by using analysis of variance of surface electromyogram (sEMG) [15].…”

Section: Introductionmentioning

confidence: 99%

Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations

et al. 2015

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The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise. All participants were performed a maximally exercise session in a motor-driven treadmill by using standard Bruce protocol which is the most widely used test to predict functional capacity. The method used in the present study consists of pre-processing, determination of the energy value based on wavelet packet transform, and classification phases. The results of the study demonstrated that changes in the MMG 176-234 Hz and EMG 254-313 Hz bands are critical to determine for muscle fatigue occurred following maximally exercise session. In conclusion, our study revealed that an algorithm with EMG and MMG combination based on frequency spectrum is more effective for the detection of muscle fatigue than EMG or MMG alone.

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Detection of Muscle Fatigue by the Surface Electromyogram and Its Application

Cited by 17 publications

References 2 publications

Port-based modeling of human-robot collaboration towards safety-enhancing energy shaping control

Port-based modeling of human-robot collaboration towards safety-enhancing energy shaping control

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations

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