extended-abstract

Improving heart rate variability measurements from consumer smartwatches with machine learning

Authors:

Martin Maritsch,

Caterina Bérubé,

Stefan Feuerriegel,

Tobias Kowatsch,

Felix WortmannAuthors Info & Claims

UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

Pages 934 - 938

https://doi.org/10.1145/3341162.3346276

Published: 09 September 2019 Publication History

Abstract

The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements.

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Index Terms

Improving heart rate variability measurements from consumer smartwatches with machine learning

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Published In

cover image ACM Conferences

UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

September 2019

1234 pages

ISBN:9781450368698

DOI:10.1145/3341162

General Chairs:
Robert Harle
Cambridge University
,
Katayoun Farrahi
University Of Southampton
,
Nicholas Lane
University Of Oxford And Samsung Ai

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 09 September 2019

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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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UbiComp '19

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UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 9 - 13, 2019

London, United Kingdom

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https://doi.org/10.2196/53714
Tao MHuang HGao JCao YZhuang J(2024)The effects of combining visual-auditory stimuli with exercise on short-term affect improvement: a randomized controlled trialScientific Reports10.1038/s41598-024-69578-y14:1Online publication date: 16-Aug-2024
https://doi.org/10.1038/s41598-024-69578-y
Yang DGao BHuang LChen BChen Y(2024)Wearable Long-Term Graph Learning for Non-invasive Mental Health EvaluationNew Approaches for Multidimensional Signal Processing10.1007/978-981-97-0109-4_15(191-203)Online publication date: 29-Jun-2024
https://doi.org/10.1007/978-981-97-0109-4_15
Parlato SCentracchio JEsposito DBifulco PAndreozzi E(2023)ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological SubjectsSensors10.3390/s2319811423:19(8114)Online publication date: 27-Sep-2023
https://doi.org/10.3390/s23198114
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Jiang ZVan Zoest VDeng WNgai ELiu J(2023)Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A SurveyIEEE Internet of Things Journal10.1109/JIOT.2023.331315810:24(21959-21981)Online publication date: 15-Dec-2023
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