Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics
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...
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| Format: | Conference or Workshop Item |
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2015
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| _version_ | 1796860666506641408 |
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| author | Che Hassan, M. Z. Khalid, P. I. Kamaruddin, N. A. Ishak, N.A. Harun, M. |
| author_facet | Che Hassan, M. Z. Khalid, P. I. Kamaruddin, N. A. Ishak, N.A. Harun, M. |
| author_sort | Che Hassan, M. Z. |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T19:44:43Z |
| format | Conference or Workshop Item |
| id | utm.eprints-59212 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:44:43Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | utm.eprints-592122021-12-14T07:23:25Z http://eprints.utm.my/59212/ Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics Che Hassan, M. Z. Khalid, P. I. Kamaruddin, N. A. Ishak, N.A. Harun, M. TK Electrical engineering. Electronics Nuclear engineering 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. 2015 Conference or Workshop Item PeerReviewed Che Hassan, M. Z. and Khalid, P. I. and Kamaruddin, N. A. and Ishak, N.A. and Harun, M. (2015) Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics. In: 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014, 8 - 10 December 2014, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1109/IECBES.2014.7047587 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Che Hassan, M. Z. Khalid, P. I. Kamaruddin, N. A. Ishak, N.A. Harun, M. Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title | Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title_full | Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title_fullStr | Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title_full_unstemmed | Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title_short | Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| title_sort | derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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