Modeling and Predicting the Human Heart Rate During Running Exercise

  • Matthias FüllerEmail author
  • Ashok Meenakshi Sundaram
  • Melanie Ludwig
  • Alexander Asteroth
  • Erwin Prassler
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 578)


The positive influence of physical activity for people at all life stages is well known. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract the increase of cardiovascular diseases in our aging society. An easy and good measure of the cardiovascular feedback is the heart rate. Being able to model and predict the response of a subject’s heart rate on work load input allows the development of more advanced smart devices and analytic tools. These tools can monitor and control the subject’s activity and thus avoid overstrain which would eliminate the positive effect on the cardiovascular system. Current heart rate models were developed for a specific scenario and evaluated on unique data sets only. Additionally, most of these models were tested in indoor environments, e.g. on treadmills and bicycle ergometers. However, many people prefer to do sports in outdoors environments and use their smart phone to record their training data. In this paper, we present an evaluation of existing heart rate models and compare their prediction performance for indoor as well as for outdoor running exercises. For this purpose, we investigate analytical models as well as machine learning approaches in two training sets: one indoor exercise set recorded on a treadmill and one outdoor exercise set recorded by a smart phone.


Prediction Performance Support Vector Regression Smart Phone Heart Rate Response Machine Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the on-going support of the Bonn-Aachen International Center for Information Technology. Furthermore, the authors would like to thank the subject for his support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matthias Füller
    • 1
    Email author
  • Ashok Meenakshi Sundaram
    • 1
  • Melanie Ludwig
    • 1
  • Alexander Asteroth
    • 1
  • Erwin Prassler
    • 1
  1. 1.Bonn-Rhein-Sieg University of Applied SciencesSankt AugustinGermany

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