Google Research

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

  • Sean K Bae
  • Silviu Borac
  • Yunus Emre
  • Jonathan Wang
  • Jiang Wu
  • Mehr Kashyap
  • Si-Hyuck Kang
  • Liwen Chen
  • Melissa Moran
  • Julie Cannon
  • Eric Steven Teasley
  • Allen Chai
  • Yun Liu
  • Neal Wadhwa
  • Mike Krainin
  • Miki Rubinstein
  • Alejandra Maciel
  • Mike McConnell
  • Shwetak Patel
  • Greg Corrado
  • Jim Taylor
  • Jiening Zhan
  • Ming Po
Nature Communications Medicine (2022)

Abstract

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

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