Skin Tone, Confidence, and Data Quality of Heart Rate Sensing in WearOS Smartwatches

Ishita Ray, Daniyal Liaqat, Moshe Gabel, Eyal de Lara

6th IEEE PerCom Workshop on Pervasive Health Technologies, Virtual, March 2021



Smartwatches can collect heart rate data unobtrusively and continuously, making them a promising tool for conducting long term studies, monitoring chronic conditions, and providing timely intervention. Healthcare applications, however, require us to understand the reliability of collected readings, both in terms of quality and quantity. The accuracy of optical heart rate (HR) measurements has been studied extensively in recent years, identifying several common causes of errors. For example, previous research has demonstrated that inaccurate HR readings occur more frequently in dark skin as compared to light skin due to melanin absorption. Smartwatches therefore implement a confidence mechanism to estimate reliability of HR readings. We study the effect of skin tone on the reliability of confidence estimation of seven consumer-grade WearOS smartwatches. We find that some watches systematically underestimate the reliability of HR readings taken from dark skin, despite no substantial difference in actual error. This results in significantly fewer data points for people with darker skin tones, which can bias downstream applications. We also report a wide variation in how watches implement the same WearOS API for HR collection, with implications for researchers that intend to use them for studies.