DocumentCode :
3565489
Title :
Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics
Author :
Che Hassan, Mohd Z. ; Khalid, Puspa I. ; Kamaruddin, Nurul A. ; Ishak, Nurul A. ; Harun, Mokhtar
Author_Institution :
Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia
fYear :
2014
Firstpage :
662
Lastpage :
666
Abstract :
Biceps brachii muscle which attached to the forearm bone is one of the important muscles to the athletes who involve in sports like badminton, tennis and volleyball. Repetition of the arm such as throwing and hitting can lead to muscle fatigue. This physiological phenomenon needs to be monitored and well controlled especially in athletes training. The purpose of this study was to formulate a simple muscle fatigue index for the biceps brachii muscles. Ten male badminton players were chosen to be the subjects for this study. Each subject was asked to do dynamic contraction by lifting 5 kilogram dumbbell. This exercise is called biceps curl exercise and the subjects were asked to repeat the task for one minute and thirty seconds. The electromyogram signal was recorded using Neuroprax EEG device. For that purpose, monopolar surface electrodes were attached to the biceps muscle of the subject. The electromyogram signals were then processed using MATLAB software. Four parameters in time domain were extracted; Zero Crossings (ZC), Root Mean Square (RMS), Mean Absolute Value (MAV), and Variance (VAR). Except for zero crossings (ZC), all other parameters showed significant difference between fatigue signal and non-fatigue signal (p-value <; 0.001). RMS was found to correlate very well with MAV (0.999). The study concludes that several temporal characteristics from electromyogram signal could be used in the formulation of biceps muscle fatigue index, supporting its use in monitoring muscle endurance.
Keywords :
biomechanics; biomedical electrodes; bone; electroencephalography; electromyography; fatigue; sport; Absolute Value Variance; MATLAB software; MAV; Mean Absolute Value; Neuroprax EEG device; RMS; Root Mean Square; VAR; ZC; Zero Crossings; arm repetition; athlete training; badminton; biceps brachii muscles; biceps curl exercise; biceps muscle fatigue index; dumbbell lifting; electromyogram signals; forearm bone; hitting; mass 5 kg; monopolar surface electrodes; muscle endurance monitoring; nonfatigue signal; sports; surface electromyography temporal characteristics; tennis; throwing; time 90 s; time domain; volleyball; Electrodes; Electromyography; Fatigue; Indexes; Muscles; Predictive models; Reactive power;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on
Type :
conf
DOI :
10.1109/IECBES.2014.7047587
Filename :
7047587
Link To Document :
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