Open Access

ARTICLE

Heart Rate Detection Using SVM Based on Video Imagery

Wu Zeng¹, Yi Sheng^1,*, Qiuyu Hu¹, Zhanxiong Huo¹, Yingge Zhang¹, Yuxuan Xie²

1 School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, 430000, China
2 Gina Cody School of Engineering and Computer Science, Concordia University, W. Montreal, Quebec, H3G 1M8, Canada

* Corresponding Author: Yi Sheng. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 377-387. https://doi.org/10.32604/iasc.2022.017748

Received 09 February 2021; Accepted 09 May 2021; Issue published 26 October 2021

Abstract

According to the World Health Organization, the death rate of cardiovascular diseases ranks first in the composition of disease deaths. Research shows that the heart rate can be employed as an important physiological parameter to measure the health status of people’s cardiac health. A pressure pulse is formed by the periodic beating and contraction of the heart, so its rate and the pressure pulse signal have a distinct synchronous periodicity. Certain wavelengths of light are known to be absorbed by the capillaries in the human skin, where this absorption fluctuates in accordance with the heartbeat as the capillary blood volume changes. Therefore, the intensity of the reflected light on the skin surface changes periodically, as manifested by a change of skin color. A dynamic target tracking algorithm was used for tracking the region of interest (ROI) in real time, where with this approach multiple targets can be monitored simultaneously. Our approach uses Photoplethysmography (IPPG) imaging technology, in conjunction with an ordinary camera to capture subtle periodic changes of intensity of reflected light from the surface skin. We then use a Support Vector Machine (SVM) algorithm for the video image data. The results of our research show that heart rate information of subjects can be detected quickly and accurately even when monitoring multiple targets.

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Keywords

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