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Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1547-4386
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2020 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 67, no 1, p. 88-98, article id 8715455Article in journal (Refereed) Published
Abstract [en]

Objective: In this paper, four physiological parameters, i.e., heart rate (HR), inter-beat-interval (IBI), heart rate variability (HRV), and oxygen saturation (SpO2), are extracted from facial video recordings. Methods: Facial videos were recorded for 10 min each in 30 test subjects while driving a simulator. Four regions of interest (ROIs) are automatically selected in each facial image frame based on 66 facial landmarks. Red-green-blue color signals are extracted from the ROIs and four physiological parameters are extracted from the color signals. For the evaluation, physiological parameters are also recorded simultaneously using a traditional sensor 'cStress,' which is attached to hands and fingers of test subjects. Results: The Bland Altman plots show 95% agreement between the camera system and 'cStress' with the highest correlation coefficient R = 0.96 for both HR and SpO2. The quality index is estimated for IBI considering 100 ms R-peak error; the accumulated percentage achieved is 97.5%. HRV features in both time and frequency domains are compared and the highest correlation coefficient achieved is 0.93. One-way analysis of variance test shows that there are no statistically significant differences between the measurements by camera and reference sensors. Conclusion: These results present high degrees of accuracy of HR, IBI, HRV, and SpO2 extraction from facial image sequences. Significance: The proposed non-contact approach could broaden the dimensionality of physiological parameters extraction using cameras. This proposed method could be applied for driver monitoring application under realistic conditions, i.e., illumination, motion, movement, and vibration.

Place, publisher, year, edition, pages
IEEE Computer Society , 2020. Vol. 67, no 1, p. 88-98, article id 8715455
Keywords [en]
Ambient illumination, driver monitoring, motion, movement, non-contact, physiological parameters, vibration, Cameras, Extraction, Heart, Video recording, Physiological models
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-46689DOI: 10.1109/TBME.2019.2908349ISI: 000505526300009Scopus ID: 2-s2.0-85077175941OAI: oai:DiVA.org:mdh-46689DiVA, id: diva2:1384236
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-23Bibliographically approved

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Rahman, HamidurAhmed, Mobyen UddinBegum, Shahina

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