Data-driven modeling of fatigue effects following repeated muscular contractions
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We propose a novel dynamical model for describing muscular fatigue and cramping following repeated muscular contractions in the case of female Kegel exercising. The proposed compartmental model adds opportune parameters that capture fatiguing effects while exercising, and extends them by adding a compartment modeling cramping muscles. The extended model can capture temporal dynamics effecting both the baseline muscular pressure of the patients and the peak pressures exerted while exercising, which cannot be captured with the models previously presented in the literature. However, this modification incurs a loss of identifiability of the model. Thus, we devise an opportune ad-hoc learning approach to solve the identification problem in a computationally efficient way. Quantitatively, we are then able to show that the adaptation to the model, improves the predictive capabilities for data that exhibit a cramping effect. We note that, the variability of the cramping on a daily basis leads to great variability in the results.