Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification

Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simone Orcioni, Claudio Turchetti

2016

Abstract

Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects’ wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.

Paper Citation

in Harvard Style

Biagetti G., Crippa P., Falaschetti L., Orcioni S. and Turchetti C. (2016). Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 467-474. DOI: 10.5220/0005755304670474

in Bibtex Style

@conference{icpram16,
author={Giorgio Biagetti and Paolo Crippa and Laura Falaschetti and Simone Orcioni and Claudio Turchetti},
title={Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={467-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005755304670474},
isbn={978-989-758-173-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Motion Artifact Reduction in Photoplethysmography using Bayesian Classification for Physical Exercise Identification
SN - 978-989-758-173-1
AU - Biagetti G.
AU - Crippa P.
AU - Falaschetti L.
AU - Orcioni S.
AU - Turchetti C.
PY - 2016
SP - 467
EP - 474
DO - 10.5220/0005755304670474