Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review

Abstract

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 techniques. 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: Institute 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 studies 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 learning 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 implement 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.