Quantifying exercise-induced muscle fatigue by machine learning
| Title | Quantifying exercise-induced muscle fatigue by machine learning |
|---|---|
| Summary | Exploring machine learning methods on an EMG muscle fatigue pipeline |
| Keywords | EMG, Machine Learning, Fatigue |
| TimeFrame | |
| References | Yousif, 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. |
| Prerequisites | |
| Author | |
| Supervisor | Jens Lundström |
| Level | Master |
| Status | Open |
Electromyography (EMG), the measurement of the electrical activity in the muscles as a response to nerve stimulation is an advanced and prominent tool for assessing muscle fatigue. During sports it is important to assess exactly when and to which extent muscle fatigue occurs in order to optimize recovery, effort distribution, and planning of the training process. Relative to manual rules machine learning has been shown to be highly effective to characterize and detect muscle fatigue, yet a few challenges remain despite the success of surface-based EMG. Variability between subjects and sensor placements pose signals complexity and analysis could potentially benefit from the more recent and advanced machine learning methods such as advanced time-series models and the concept of attention. This project is about surveying and exploring state-of-the-art methods and systematically, theoretically, and practically test the applicability and performance of more recent machine learning methods on an existing EMG to muscle fatigue pipeline. Also, the use of XAI for the characterization of the signals is desired to develop the interpretation of the developed models. You will be working with a local startup sports-tech company and the work also included to do hands-on experiments with sensors and embedded systems.
