Investigating sources of inaccuracy in wearable optical heart rate sensors.

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As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.





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Bent, Brinnae, Benjamin A Goldstein, Warren A Kibbe and Jessilyn P Dunn (2020). Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ digital medicine, 3(1). p. 18. 10.1038/s41746-020-0226-6 Retrieved from

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Brinnae Bent

Executive in Residence in the Engineering Graduate and Professional Programs

As a leader in bridging the gap between research and industry in machine learning, I have led projects and developed algorithms for the largest companies in the world. More importantly, I have built algorithms that have meaningful impacts - from helping people walk to noninvasively monitoring glucose.

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Warren Alden Kibbe

Professor in Biostatistics & Bioinformatics

Warren A. Kibbe, PhD, is chief for Translational Biomedical Informatics in the Department of Biostatistics and Bioinformatics and Chief Data Officer for the Duke Cancer Institute. He joined the Duke University School of Medicine in August after serving as the acting deputy director of the National Cancer Institute (NCI) and director of the NCI’s Center for Biomedical Informatics and Information Technology where he oversaw 60 federal employees and more than 600 contractors, and served as an acting Deputy Director for NCI. As an acting Deputy Director, Dr. Kibbe was involved in the myriad of activities that NCI oversees as a research organization, as a convening body for cancer research, and as a major funder of cancer research, funding nearly $4B US annually in cancer research throughout the United States. 


Jessilyn Dunn

Assistant Professor of Biomedical Engineering

Developing new tools and infrastructure for multi-modal biomedical data integration to drive precision/personalized methods for early detection, intervention, and prevention of disease.

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