Authors:
Melanie Ludwig
;
Ashok Meenakshi Sundaram
;
Matthias Füller
;
Alexander Asteroth
and
Erwin Prassler
Affiliation:
Bonn-Rhein-Sieg Univ. of Applied Sciences, Germany
Keyword(s):
Modeling and Predicting Behavior of Cardiovascular System, Adaptive Generation of Training Plans, Automated Generation of Training Plans, Model-predictive Control of Smart Training Devices.
Abstract:
With the increasing average age of the population in many developed countries, afflictions like cardiovascular
diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can
counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous
research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the
heart rate from work load information is an essential part in models used for training control. Most heart-rate-based
models are described in the context of specific scenarios, and have been evaluated on unique datasets
only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system
under a certain strain, and compare their predictive performance. For this purpose, we investigated some
analytical models as well as some machine learning approaches in two scenarios: prediction over a certain
time horizon in
to the future, and estimation of the relation between work load and heart rate over a whole
training session.
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