Improving heart rate variability measurements from consumer smartwatches with machine learning
Maritsch M, Berube C, Kraus M, Lehmann V, Zuger T, Feuerriegel S, Kowatsch T, Wortmann F (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Association for Computing Machinery, Inc
Pages Range: 934-938
Conference Proceedings Title: UbiComp/ISWC 2019- - 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
Event location: London
ISBN: 9781450368698
Abstract
The reactions of the human body to physical exercise, psy-chophysiological 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.
Authors with CRIS profile
Involved external institutions
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
Universität St.Gallen
Switzerland (CH)
Inselspital, Universitätsspital Bern
Switzerland (CH)
Switzerland (CH)
Universität St.Gallen
Switzerland (CH)
Inselspital, Universitätsspital Bern
Switzerland (CH)
How to cite
APA:
Maritsch, M., Berube, C., Kraus, M., Lehmann, V., Zuger, T., Feuerriegel, S.,... Wortmann, F. (2019). Improving heart rate variability measurements from consumer smartwatches with machine learning. In UbiComp/ISWC 2019- - 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 (pp. 934-938). London, GB: Association for Computing Machinery, Inc.
MLA:
Maritsch, Martin, et al. "Improving heart rate variability measurements from consumer smartwatches with machine learning." Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019, London Association for Computing Machinery, Inc, 2019. 934-938.
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