Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2016 (2016), Article ID 6830152, 7 pages
http://dx.doi.org/10.1155/2016/6830152
Research Article

Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
3Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China
4Department of Physics and Materials Science, City University of Hong Kong, Kowloon, Hong Kong
5Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

Received 10 June 2015; Revised 18 November 2015; Accepted 18 November 2015

Academic Editor: Banshi D. Gupta

Copyright © 2016 Rong-Chao Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI), which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR), spectral energy ratio (SER), template matching (TM), temporal difference (TD), and gradient (GRAD). Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm) and smaller limits of agreement (<3 bpm). The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.