Abstract
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.
Highlights
Fatigue is defined as the inability of the respective muscles to contract and perform a specific procedure over a long period of time [1,2]
It was clearly shown that the integrated EMG (IEMG) exhibited a positive slope in muscle fatigue condition
An algorithm employing a new fatigue index was proposed for the objective detection of muscle fatigue
Summary
Fatigue is defined as the inability of the respective muscles to contract and perform a specific procedure over a long period of time [1,2]. This inability happens because of the reduction of the central motor drive of the muscle, which reduces the amount of force being produced, and results in the experience of pain and fatigue [3]. The subjective detection of muscle fatigue is reported by the individuals themselves, when no type of measurement is applied [6]. 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]. 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]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
