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

A depth camera motion analysis framework for tele-rehabilitation : motion capture and person-centric kinematics analysis

With increasing importance given to telerehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standard optical systems, without compromising on accuracy. Novel signal processing and computer vision algorithms are proposed to determine motion patterns of interest from infrared and depth data. In order to demonstrate the proposed framework’s suitability for rehabilitation, we developed a gait analysis application that depends on the underlying motion capture sub-system. Each subject’s individual kinematics parameters, which are unique to that subject, are calculated and these are stored for monitoring individual progress of the clinical therapy. Experiments were conducted on 14 different subjects, 5 healthy and 9 stroke survivors. The results show very close agreement of the resulting relevant joint angles with a 12-camera based VICON system, a mean error of at most 1.75% in detecting gait events w.r.t the manually generated ground-truth, and significant performance improvements in terms of accuracy and execution time compared to a previous Kinect-based system