Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques.

Autor: Pagano TP; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Dos Santos LL; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Santos VR; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Sá PHM; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Bonfim YDS; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Paranhos JVD; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Ortega LL; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Nascimento LFS; Computational Modeling Department, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Santos A; HP Inc. Brazil R&D, Porto Alegre 90619-900, Rio Grande do Sul, Brazil., Rönnau MM; HP Inc. Brazil R&D, Porto Alegre 90619-900, Rio Grande do Sul, Brazil., Winkler I; Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil., Nascimento EGS; Department of Management and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Bahia, Brazil.; Faculty of Engineering and Physical Sciences, School of Computer Science and Electronic Engineering, Surrey Institute for People-Centred AI, University of Surrey, Guildford GU2 7XH, UK.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Dec 05; Vol. 22 (23). Date of Electronic Publication: 2022 Dec 05.
DOI: 10.3390/s22239486
Abstrakt: Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient's heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.
Databáze: MEDLINE
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