LAUSR

Providing an easy-to-use and non-invasive heart rate estimation system for home healthcare is imperative for detecting health deterioration in the early stages and for providing preventive treatments. To this end, we propose a ballistocardiographic method that estimates heart rate by analysing face videos and exploiting cyclic head motions caused by blood pumping through the carotid arteries during heartbeats. The proposed method addresses hand motion artefacts that occur while tracking head motions on a mobile platform. This is achieved by training an auto-encoder that retrieves a heart rate-related signal by learning consistent and statistically dominant relationships between facial landmarks. Moreover, unlike conventional methods, where a feature point tracker was used to track the facial landmarks, we propose using region-based tracking to increase tracking robustness in challenging lighting conditions. Experimental results demonstrate that the proposed method performs best in challenging light conditions and can robustly manage hand motion artefacts on a mobile platform.