Project : BayesHeart

A Probabilistic Approach for Robust, Low-Latency Heart Rate Monitoring on Camera Phones

Figure 1: Scenarios for Implicit Heart Rate Tracking
Figure 2: Local Trends as Observations
Figure 3: Cardiac cycle & distinct phase extraction


Recent technological advances have demonstrated the feasibility of measuring people’s heart rates through commodity cameras by capturing users’ skin transparency changes, color changes, or involuntary motion. However, such raw image data collected during everyday interactions (e.g. gaming, learning, and fitness training) is often noisy and intermittent, especially in mobile contexts. Such interference causes increased error rates, latency, and even detection failures for most existing algorithms. In this paper, we present BayesHeart, a probabilistic algorithm that extracts both heart rates and distinct phases of the cardiac cycle directly from raw fingertip transparency signals captured by camera phones. BayesHeart is based on an adaptive hidden Markov model, requires minimal training data and is user-independent. Through a comparative study of twelve state-of-the-art algorithms covering the design space of noise reduction and pulse counting, we found that BayesHeart outperforms existing algorithms in both accuracy and speed for noisy, intermittent signals.


  • Fan, X., and Wang, J., BayesHeart: A Probabilistic Approach for Robust, Low-Latency Heart Rate Monitoring on Camera Phones. In Proceedings of 20th ACM Conference on Intelligent User Interfaces (IUI 2015), Atalanta, GA, March 29 – April 1, 2015. ( pdf )

Please also refer to the publications in the AttentiveLearner project on the modeling and use of physiological signals implicitly collected from mobile interactions.


Source Code (BSD License) Dataset