Eyes as Windows to the Heart: Predicting Heart Rate from Pupillometric Features

Kevin Kristofer Kosasih, Carl Daniel Karlsson, Thilini Savindya Karunarathna, Zilu Liang, Zilu Liang

2025

Abstract

Heart rate is a key indicator of health, typically measured through skin-contact methods such as electrocardiograms (ECG) or photoplethysmograms (PPG). However, these methods may not be comfortable for everyone, prompting interest in non-contact alternatives. Eye tracking presents a promising solution, as the autonomic nervous system links the eyes to heart rate. This research develops heart rate prediction models based on pupillometric features. We conducted data collection experiments to build a dataset of multi-modal measurements of pupillometric data and heart rate from 10 subjects at high sampling rates. Several regression models, including linear regression, ridge regression, random forest regression, and XGBoost regression, were trained on the dataset. The random forest model achieved the best performance with a R2 of 0.457 and a root mean square error (RMSE) of 9 beats per minute, representing a 52.3% improvement over the state-of-the-art. Future work should focus on expanding the dataset, refining feature extraction and selection, and incorporating 3D pupillometric data to enhance model accuracy and applicability.

Paper Citation

in Harvard Style

Kosasih K., Karlsson C., Karunarathna T. and Liang Z. (2025). Eyes as Windows to the Heart: Predicting Heart Rate from Pupillometric Features. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 968-975. DOI: 10.5220/0013385000003911

in Bibtex Style

@conference{healthinf25,
author={Kevin Kosasih and Carl Karlsson and Thilini Karunarathna and Zilu Liang},
title={Eyes as Windows to the Heart: Predicting Heart Rate from Pupillometric Features},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={968-975},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013385000003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Eyes as Windows to the Heart: Predicting Heart Rate from Pupillometric Features
SN - 978-989-758-731-3
AU - Kosasih K.
AU - Karlsson C.
AU - Karunarathna T.
AU - Liang Z.
PY - 2025
SP - 968
EP - 975
DO - 10.5220/0013385000003911
PB - SciTePress