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Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: a systematic review
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Advisor(s)
Abstract(s)
Background: Consumer-level cameras have provided an advantage of designing cost-effective, non-contact
physiological parameters estimation approaches which is not possible with gold standard estimation tech niques. This encourages the development of non-contact estimation methods using camera technology.
Therefore, this work aims to present a systematic review summarizing the currently existing face-based
non-contact methods along with their performance.
Methods: This review includes all heart rate (HR) and oxygen saturation (SpO2) studies published in journals and a few reputed conferences, which have compared the proposed estimation methods with one or
more standard reference devices. The articles were collected from the following research databases: In stitute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science (WoS), Science Direct, and
Association of Computer Machinery (ACM) digital library. All database searches were completed on May
20, 2021. Each study was assessed using a finite set of identified factors for reporting bias.
Results: Out of 332 identified studies, 32 studies were selected for the final review. Additionally, 18 studies
were included by thoroughly checking these studies. 3 out of 50 (6%) studies were performed in clinical
conditions, while the remaining studies were carried out on a healthy population. 42 out of 50 (84%)
studies have estimated HR, while 5/50 (10%) studies have measured SpO2 only. The remaining three stud ies have estimated both parameters. The majority of the studies have used 1–3 min videos for estimation.
Among the estimation methods, Deep Learning and Independent component analysis (ICA) were used by
11/42 (26.19%) and 9/42 (21.42%) studies, respectively. According to the Bland-Altman analysis, only 8/45
(17.77%) HR studies achieved the clinically accepted error limits whereas, for SpO2, 4/5 (80%) studies have
matched the industry standards (±3%).
Discussion: Deep Learning and ICA have been predominantly used for HR estimations. Among deep learn ing estimation methods, convolutional neural networks have been employed till date due to their good
generalization ability. Most non-contact HR estimation methods need significant improvements to im plement these methods in a clinical environment. Furthermore, these methods need to be tested on the
subjects suffering from any related disease. SpO2 estimation studies are challenging and need to be tested
by conducting hypoxemic events. The authors would encourage reporting the detailed information about
the study population, the use of longer videos, and appropriate performance metrics and testing under
abnormal HR and SpO2 ranges for future estimation studies.
Description
Keywords
Non-contact estimation approaches Physiological parameters Blood volume pulse Heart rate Oxygen saturation . Faculdade de Ciências Exatas e da Engenharia
Citation
Publisher
Elsevier BV