VitaSi: A real-time contactless vital signs estimation system. (October 2021)

• Record Type: Journal Article
• Authors: Wang, Haopeng; Zhou, Yufan; Saddik, Abdulmotaleb El
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Vital signs estimation -- Convolutional neural network -- Phase-based video motion processing -- Non-contact -- Android application; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107392
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The non-contact monitoring of vital signs, especially the Heart Rate (HR) and Breathing Rate (BR), using facial video is becoming increasingly important. Although, researchers have made considerable progress in the past few years, there are still some limitations to the technology, such as the lack of challenging datasets, the time consuming nature of the estimation process, and non-portability of the system. In this paper, we proposed a new framework for estimating HRs and BRs by combining a Convolutional Neural Network (CNN) with the Phase-based Video Motion Processing (PVMP) algorithm. The experimental results show that our approach achieves better performance. Meanwhile, we introduce a new challenging dataset with fewer constraints, such as large movements, facial expressions and light interference. In addition, we developed a new Android application, which works in real time and offline, based on a CNN for HR and BR estimations. Graphical abstract: Highlights: A non-contact system is proposed to monitor heart rate and breathing rate. Phase-based video motion processing is used to attenuate noise and magnify signals. Convolutional neural network is used to estimate heart rate and breathing rate. Mean absolute error reduces by 8.3% compared with state-of-the-art methods. A new less-constrained dataset for the heart rate and breathing rate is proposed.