Abstract
Wearable heart rate (HR) sensing devices are increasingly used to monitor human health. The availability and the quality of the HR measurements may however be affected by the body location at which the device is worn. The goal of this paper is to compare HR data collected from different devices and body locations and to investigate their interchangeability at different stages of the data analysis pipeline. To this goal, we conduct a data collection campaign and collect HR data from three devices worn at different body positions (finger, wrist, chest): The Oura ring, the Empatica E4 wristband and the Polar chestbelt. We recruit five participants for 30 nights and gather HR data along with self-reports about sleep behavior. We compare the raw data, the features extracted from this data over different window sizes, and the performance of models that use these features in recognizing sleep quality. Raw HR data from the three devices show a high positive correlation. When features are extracted from the raw data, though, both small and significant differences can be observed. Ultimately, the accuracy of a sleep quality recognition classifier does not show significant differences when the input data is derived from the Oura ring or the E4 wristband. Taken together, our results indicate that the HR measurements collected from the considered devices and body locations are interchangeable. These findings open up new opportunities for sleep monitoring systems to leverage multiple devices for continuous sleep tracking.
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Notes
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Please contact the corresponding author of the paper to make a request regarding the dataset.
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Heart Rate from multiple devices and body positions for Sleep measurement.
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Oura Ring: https://ouraring.com; Apple watch: https://www.apple.com/watch/; THIM ring: https://thim.io; Fitbit: https://www.fitbit.com/; Actiwatch: https://www.usa.philips.com/healthcare/sites/actigraphy; Samsung Gear Sport watch: https://www.samsung.com/us/watches/galaxy-watch4/.
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Acknowledgement
This contribution is supported by the Swiss National Science Foundation (SNSF) through the grant 205121 _197242 for the project “PROSELF: Semi-automated Self-Tracking Systems to Improve Personal Productivity”. Shkurta Gashi is supported by an ETH AI Center postdoctoral fellowship.
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Abdalazim, N., Larraza, J.A.A., Alchieri, L., Alecci, L., Santini, S., Gashi, S. (2023). Heart Rate During Sleep Measured Using Finger-, Wrist- and Chest-Worn Devices: A Comparison Study. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_2
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