dc.contributor.author	Mohamad Ishak, Nurul Ain
dc.date.accessioned	2023-05-10T01:38:18Z
dc.date.available	2023-05-10T01:38:18Z
dc.date.issued	2017
dc.description	Thesis (Ph.D (Biomedical Engineering))
dc.description.abstract	Many studies have been conducted to track muscle fatigue and to understand the mechanisms that contribute to the deterioration of muscle performance. Electromyography fatigue threshold (EMGFT) and Integrated Electromyography (IEMG) are two techniques that have been applied to determine the Onset of Muscle Fatigue (OMF) by depending on the percentage force output and amplitude respectively. Nevertheless, force and amplitude are correlated with one another during fatigue. Joint Analysis of EMG Spectrum and Amplitude (JASA) is commonly used to discriminate force-related from fatigue induced EMG changes. However, the length of signal affects the performance of JASA in discriminating fatigue signal. Apart from that, JASA has not been used to detect OMF. Thus, the purpose of this study is to determine the OMF region by applying JASA on the segmented EMG signal. Surface EMG signals were recorded from 30 college students while they were performing isometric contractions of Biceps Brachii muscles for 2 minutes. Each recorded signal was segmented into 15-second time interval. Root Mean Square (RMS) and Mean Frequency (MNF) were used as the muscle fatigue indicators. The indicators were extracted from 3-second epoch length within each segment. A polynomial regression model was applied to describe the trends of the indicators in a segment. The first segment that simultaneously showed a decrease in the frequency and an increase in the amplitude of a sEMG signal with correlation coefficient r = 0.7 was classified as the region where the OMF occurred. Out of 30 subjects, 20 subjects (67%) either admitted to experience muscle discomfort and at the same time the OMF region was also detected or vice-versa. For the other 10 subjects, the OMF region was able to be detected in 90% of them but due to better endurance levels, they required longer time to experience muscle discomfort. The temporal-spectral fatigue indicator (Instantaneous Mean Frequency (iMNF)) was used to determine the reliability of the developed technique. The decrement of iMNF on the detected OMF region showed high correlation coefficient (r > 0.6). The subjects were also asked to perform dynamic contractions for 2 minutes. The proposed technique was applied to the recorded signals and the OMF was detected in 24 subjects. Eighteen of them (72%) acknowledged that they had experienced muscle discomfort. Fourteen out of 18 subjects felt muscle discomfort after OMF was detected. The results indicate that muscle discomfort develops gradually after the onset of muscle fatigue. For handwriting activity, 4 subjects were asked to write for 5 minutes while the sEMG signals were captured from Flexor Carpi Radialis muscle (small muscle). Out of 4 subjects, all of them showed an increment in pen pressure, and 75% of them showed an increment in the writing speed after detecting OMF region. This study concludes that the proposed technique is feasible to detect the OMF; not only during isometric contraction but also during dynamic contraction. The technique also has the potential to be applied to small muscle contraction
dc.description.sponsorship	Faculty of Biosciences and Medical Engineering
dc.identifier.uri	http://openscience.utm.my/handle/123456789/376
dc.language.iso	en
dc.publisher	Universiti Teknologi Malaysia
dc.subject	Biosciences and medical engineering
dc.subject	Electromyography
dc.title	Detection of onset muscle fatigue based on joint analysis of surface electromyography spectrum and amplitude
dc.type	Thesis
dc.type	Dataset

Detection of onset muscle fatigue based on joint analysis of surface electromyography spectrum and amplitude

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