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On modeling the cardiovascular system and predicting the human heart rate under strain

  • 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 into the future, and estimation of the relation between work load and heart rate over a whole training session.

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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgingWell), May 20-22, 2015, in Lisbon, Portugal
First Page:106
Last Page:117
ISBN:978-989-758-102-1
DOI:https://doi.org/10.5220/0005449001060117
Publisher:SciTePress
Publication year:2015
Award:Best-Student-Paper Award
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/09/09