Digital devices based raining monitoring is widely understood to be a crucial part of modern athletes’ follow-up as it helps to assess individual response to training. Consequently, training monitoring could also reduce
the risk of overtraining syndrome, injury, illness, and benefit athletic performance.
Cassiopée Sport introduces novel technique using multi-dimensional Geometic Artifical Neural Networks technique combning objective as well as subjective athletic data to construct computationa narrative we call Predictive Geometric Activity Performance Index.(pGAPI)
pGAPI introduces complexity multi-dimensional implementation of geometric Artifical Neural Networks approach to athetic sonsory and questinaire based data.
The graph below shows pGAPI based on subjective data of a single athlete with respect to three different verions of pGAPIs. Namely, personal best performance that is supposed to be an objective measure and some subjective measures such as mood distoration or averall satisfaction, etc.
Data Interrelations In Sport Science
We consider combinations of the collected parameters rather than single parameter separately.
We considered combinations of two training and/or recovery parameters and added personal best as the third parameter in each pGAPI combination in order to measure, e.g., performance.
For each combination Artificial Neural Networks in combination with a geometric optimization is used.
The simulation below indicates a form of the resuls delivered to a personal device in terms of differen pGAPIs from Coach point of view, considering diffrerent athletes.