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Fan X, Liu F, Zhang J, Gao T, Fan Z, Huang Z, Xue W, Zhang J. Remote photoplethysmography based on reflected light angle estimation. Physiol Meas 2024; 45:035005. [PMID: 38430568 DOI: 10.1088/1361-6579/ad2f5d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 03/01/2024] [Indexed: 03/04/2024]
Abstract
Objective. In previous studies, the factors affecting the accuracy of imaging photoplethysmography (iPPG) heart rate (HR) measurement have been focused on the light intensity, facial reflection angle, and motion artifacts. However, the factor of specularly reflected light has not been studied in detail. We explored the effect of specularly reflected light on the accuracy of HR estimation and proposed an estimation method for the direction of specularly radiated light.Approach. To study the HR measurement accuracy influenced by specularly reflected light, we control the component of specularly reflected light by controlling its angle. A total of 100 videos from four different reflected light angles were collected, and 25 subjects participated in the dataset collection. We extracted angles and illuminations for 71 facial regions, fitting sample points through interpolation, and selecting the angle corresponding to the maximum weight in the fitted curve as the estimated reflected angle.Main results. The experimental results show that higher specularly reflected light compromises HR estimation accuracy under the same value of light intensity. Notably, at a 60° angle, the HR accuracy (ACC) increased by 0.7%, while the signal-to-noise ratio and Pearson correlation coefficient increased by 0.8 dB and 0.035, respectively, compared to 0°. The overall root mean squared error, standard deviation, and mean error of our proposed reflected light angle estimation method on the illumination multi-angle incidence (IMAI) dataset are 1.173°, 0.978°, and 0.773°. The average Pearson value is 0.8 in the PURE rotation dataset. In addition, the average ACC of HR measurements in the PURE dataset is improved by 1.73% in our method compared to the state-of-the-art traditional methods.Significance. Our method has great potential for clinical applications, especially in bright light environments such as during surgery, to improve accuracy and monitor blood volume changes in blood vessels.
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Affiliation(s)
- Xuanhe Fan
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - Fangwu Liu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Jinjin Zhang
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - Tong Gao
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - Ziyang Fan
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - Zhijie Huang
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - Wei Xue
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
| | - JingJing Zhang
- China University of Geosciences, Wuhan, China, School of Automation, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, People's Republic of China
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Molinaro N, Schena E, Silvestri S, Massaroni C. Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22072539. [PMID: 35408151 PMCID: PMC9002464 DOI: 10.3390/s22072539] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/16/2022] [Accepted: 03/23/2022] [Indexed: 05/05/2023]
Abstract
Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min-1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.
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Affiliation(s)
- Nunzia Molinaro
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Sergio Silvestri
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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