Improving heart rate variability measurements from consumer smartwatches with machine learning

Maritsch, Martin; Bérubé, Caterina; Kraus, Mathias; Lehmann, Vera; Züger, Thomas; Feuerriegel, Stefan; Kowatsch, Tobias; Wortmann, Felix (2019). Improving heart rate variability measurements from consumer smartwatches with machine learning. In: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM International Symposium (pp. 934-938). New York, New York, USA: ACM Press 10.1145/3341162.3346276

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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 smart-watches 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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