Refine
Departments, institutes and facilities
- Fachbereich Informatik (48)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (42)
- Fachbereich Ingenieurwissenschaften und Kommunikation (9)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (3)
- Institut für KI und Autonome Systeme (A2S) (1)
- Institut für Sicherheitsforschung (ISF) (1)
- Institute of Visual Computing (IVC) (1)
Document Type
- Conference Object (39)
- Article (11)
- Preprint (5)
- Report (5)
- Book (monograph, edited volume) (1)
- Part of a Book (1)
- Diploma Thesis (1)
- Doctoral Thesis (1)
Year of publication
Keywords
- Quality Diversity (4)
- Quality diversity (4)
- Bayesian optimization (3)
- MAP-Elites (3)
- Aerodynamics (2)
- Autoencoder (2)
- Evolutionary Computation (2)
- Evolutionary computation (2)
- Heart Rate Prediction (2)
- Lattice Boltzmann Method (2)
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.