Estimating heart rate via depth video motion tracking

Depth sensors like Microsoft Kinect can acquire partial geometric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and hence using them to deduce health metrics that require tracking subtle 3D structural details is difficult. In this paper, we propose to capture depth video using Kinect 2.0 to estimate the heart rate of a human subject; as blood is pumped to circulate through the head, tiny oscillatory head motion can be detected for periodicity analysis. Specifically, we first perform a joint bit-depth enhancement / denoising procedure to improve the quality of the captured depth images, using a graph-signal smoothness prior for regularization. We then track an automatically detected nose region throughout the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close to ground truth.