TS-CAN+: A Improved TS-CAN Architecture for Non-Contact Heart Rate Measurement

• Published in: IEEE transactions on consumer electronics Vol. 71; no. 1; pp. 1393 - 1401
• Main Authors: Li, Yihang, Huang, Junjian, Zhao, Jichao, Wu, Dayu, Zheng, Mao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Convolution; convolutional block attention module; Datasets; deep learning; Feature extraction; Heart rate; Heart rate measurement; Measurement methods; Physiology; Real time; Remote heart rate measurement; Remote monitoring; remote photoplethysmography (rPPG); Skin; Telemedicine
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2024.3424903

Heart rate is one of the most crucial physiological indicators of the human body, which can reflect the health status of the cardiovascular and cerebrovascular. Therefore, heart rate monitoring in real-time is essential for the prevention and treatment of cardiovascular and cerebrovascular diseases. Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method, which addresses the limitation of traditional heart rate measurement methods that need to contact with the subjects, and has a broad application prospect in ward vital signs monitoring and remote physiological health monitoring. How to use the rPPG method to measure heart rate more accurately is still a challenging problem. In this paper, we propose the TS-CAN+ method, which is based on TS-CAN and further incorporates convolutional block attention modules and replaces the ordinary convolution of the appearance branch with the depthwise separable convolution to improve the network accuracy. To validate the performance of our model, we conduct extensive experiments on the public PURE, UBFC-rPPG, and MMPD datasets. The experimental results indicate that compared with TS-CAN, the mean absolute error of TS-CAN+ is reduced by 37.21% in the cross-dataset test on UBFC-rPPG dataset and reduced by 12.18% on MMPD dataset.