  This report introduces VitalLens, an app that estimates vital signs such as
heart rate and respiration rate from selfie video in real time. VitalLens uses
a computer vision model trained on a diverse dataset of video and physiological
sensor data. We benchmark performance on several diverse datasets, including
VV-Medium, which consists of 289 unique participants. VitalLens outperforms
several existing methods including POS and MTTS-CAN on all datasets while
maintaining a fast inference speed. On VV-Medium, VitalLens achieves mean
absolute errors of 0.71 bpm for heart rate estimation, and 0.76 bpm for
respiratory rate estimation.
