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Quantifying exercise-induced muscle fatigue by machine learning
Keywords EMG, Machine Learning, Fatigue  +
Level Master  +
OneLineSummary Exploring machine learning methods on an EMG muscle fatigue pipeline  +
References Yousif, Hayder A., et al. "Assessment of mYousif, Hayder A., et al. "Assessment of muscles fatigue based on surface EMG signals using machine learning and statistical approaches: a review." IOP conference series: materials science and engineering. Vol. 705. No. 1. IOP Publishing, 2019. Karlik, Bekir. "Machine learning algorithms for characterization of EMG signals." International Journal of Information and Electronics Engineering 4.3 (2014): 189. Rampichini, S., Vieira, T. M., Castiglioni, P., & Merati, G. (2020). Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review. Entropy, 22(5), 529. Carroll, T. J., Taylor, J. L., & Gandevia, S. C. (2017). Recovery of central and peripheral neuromuscular fatigue after exercise. Journal of Applied Physiology, 122(5), 1068-1076. Yousefi, J., & Hamilton-Wright, A. (2014). Characterizing EMG data using machine-learning tools. Computers in biology and medicine, 51, 1-13.mputers in biology and medicine, 51, 1-13.
StudentProjectStatus Open  +
Supervisors Jens Lundström +
Title Quantifying exercise-induced muscle fatigue by machine learning  +
Categories StudentProject  +
Modification dateThis property is a special property in this wiki. 3 October 2022 08:35:51  +
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