Picture of classic books on shelf

Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history. English hosts the Centre for Literature, Culture & Place which explores literature and its relationships with geography, space, landscape, travel, architecture, and the environment.

Explore all Strathclyde Open Access research...

Estimating heart rate and rhythm via 3D motion tracking in depth video

Yang, Cheng and Cheung, Gene and Stankovic, Vladimir (2017) Estimating heart rate and rhythm via 3D motion tracking in depth video. IEEE Transactions on Multimedia, 19 (7). pp. 1625-1636. ISSN 1520-9210

[img]
Preview
Text (Yang-Cheung-Stankovic-TM-2017-Estimating-heart-rate-and-rhythm-via-3D-motion-tracking)
Yang_Cheung_Stankovic_TM_2017_Estimating_heart_rate_and_rhythm_via_3D_motion_tracking.pdf
Accepted Author Manuscript

Download (5MB) | Preview

Abstract

Low-cost depth sensors, such as Microsoft Kinect, have potential for non-intrusive, non-contact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suer from low bit-depth and high acquisition noise, and hence processing them to estimate biometrics is dicult. In this paper, we propose to capture depth video of a human subject using Kinect 2.0 to estimate his/her heart rate and rhythm (regularity); as blood is pumped from the heart to circulate through the head, tiny oscillatory head motion due to Newtonian mechanics can be detected for periodicity analysis. Specifically, we first restore a captured depth video via a joint bit-depth enhancement / denoising procedure, using a graph-signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3D motion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in two modules are consistent, improving performance of both restoration and motion tracking in the process. Third, the computed 3D motion vectors are projected onto its principal component for 1D signal analysis, composed of trend removal, band-pass filtering, and wavelet-based motion denoising. Finally, the heart rate is estimated via Welch power spectrum analysis, and the heart rhythm is computed via peak detection. Experimental results show accurate estimation of the heart rate and rhythm using our proposed algorithm as compared to rate and rhythm estimated by a portable oximeter.