Heart Rate and Oxygen Saturation Estimation from Facial Video with Multimodal Physiological Data Generation

Akamatsu, Yusuke; Onishi, Yoshifumi; Imaoka, Hitoshi

doi:10.1109/icassp43922.2022.9747109

Cited by 7 publications

(8 citation statements)

References 40 publications

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“…[81] Some machine learning-based approaches have been proposed to tickle the problem of traditional signal processing methods. Akamatsu et al [82] proposed MultiPhys and CNN models to simultaneously estimate heart rate and oxygen saturation from facial videos. In summary, there are two difficulties in the research and exploration of oxygen saturation measurement.…”

Section: Oxygen Saturationmentioning

confidence: 99%

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

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Section: Oxygen Saturationmentioning

confidence: 99%

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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“…Deep learning techniques have achieved state-of-the-art performance for the remote measurement of physiological signs such as HR [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] and RR [36,[39][40][41][42][43][44][45][46][47]. However, remote SpO2 measurement is still in its infancy, with only a few papers using convolutional neural networks (CNNs) to predict SpO2 from RGB facial videos [48][49][50]. Additionally, most existing methods are evaluated on private self-collected datasets, preventing a fair comparison of algorithmic performance [51].…”

Section: Introductionmentioning

confidence: 99%

Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning

Cheng,

Yuen,

Chen

et al. 2024

Bioengineering

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Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person’s health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial–temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial–temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.

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“…Thus, in this paper, we propose the use of EVM, as a preprocessing step to enhance the video content captured by the infrared camera. Recent research has explored and demonstrated various methods for estimating oxygen saturation levels using facial video with multimodal physiological data generation [19] or via DC and AC component extraction of a spatiotemporal map using facial videos [17].…”

Section: Introductionmentioning

confidence: 99%

A Study of Machine Learning Regression Techniques for Non-Contact SpO2 Estimation from Infrared Motion-Magnified Facial Video

Stogiannopoulos,

Cheimariotis,

Mitianoudis

2023

Information

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This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO2), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO2 monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO2 monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO2 values of the best performing method reach a low root mean squared error of 1.331 and an R2 score of 0.465 that fall within the acceptable range for these applications.

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Heart Rate and Oxygen Saturation Estimation from Facial Video with Multimodal Physiological Data Generation

Cited by 7 publications

References 40 publications

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning

A Study of Machine Learning Regression Techniques for Non-Contact SpO2 Estimation from Infrared Motion-Magnified Facial Video

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