Abstract
In sports training, fatigue prediction using surface electromyography analysis is manually monitored by human coach. Decisions rely very much on experience. Hence, the endurance training plan for an athlete needs to be individually designed by an experienced coach. The pre-designed training plan suits the athlete fitness state in general, but not in real time. Real-time muscle monitoring and feedback help in understanding every fitness states throughout the training to optimise muscle performance. This can be realized with muscle fatigue prediction using computational modelling. Due to the higher amount of motion artefact, research in isotonic muscle fatigue prediction is very much lesser than the isometric prediction. Thus, this paper investigates the Butterworth high-pass noise filter on isotonic muscle fatigue data. Three cut-off thresholds, i.e. 5 Hz, 10 Hz, and 20 Hz, were compared using the Fuzzy c-Mean Radial Basis Function Network model. Several features of time and frequency domains, i.e. the median frequency, mean frequency, mean absolute value, root mean squares, simple square integral, variance length, and waveform length were used as model predictors. The cut-off threshold at 10 Hz is the best frequency with the lowest average mean squared error of 0.0282 and best validation performance at epoch 972.
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Acknowledgment
The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and the Ministry of Higher Education, Malaysia for the financial supports given through the Research Acculturation Collaborative Effort (RACE) research grant, RACE/F3/TK12/FTMK/F00252 and UTeM Hi-Impact Short Term Grant, PJP/2016/FTMK/HI3/S01474. Appreciation is also credited to Faculty of Sport Science and Coaching, Sultan Idris Education University (UPSI) for voluntary contribution on experimental data collection.
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Ahmad Sharawardi, N.S., Choo, YH., Chong, SH., Mohamad, N.I. (2018). A Comparison of Butterworth Noise Filteration Frequency for Isotonic Muscle Fatigue Analysis. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_24
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