Estimating heart rate via depth video motion tracking

Cheng Yang, Gene Cheung, Vladimir Stankovic

Research output: Contribution to conferencePaperpeer-review

13 Citations (Scopus)
66 Downloads (Pure)


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.
Original languageEnglish
Number of pages6
Publication statusPublished - 3 Jul 2015
EventICME-2015 - Italy, Torino, Italy
Duration: 29 Jun 20153 Jul 2015




  • graph signal processing
  • health monitoring
  • image enhancement


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