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Chen W, Yi Z, Lim LJR, Lim RQR, Zhang A, Qian Z, Huang J, He J, Liu B. Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement. Front Bioeng Biotechnol 2024; 12:1420100. [PMID: 39104628 PMCID: PMC11298756 DOI: 10.3389/fbioe.2024.1420100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/27/2024] [Indexed: 08/07/2024] Open
Abstract
In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.
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Affiliation(s)
- Wei Chen
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Zhe Yi
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Lincoln Jian Rong Lim
- Department of Medical Imaging, Western Health, Footscray Hospital, Footscray, VIC, Australia
- Department of Surgery, The University of Melbourne, Melbourne, VIC, Australia
| | - Rebecca Qian Ru Lim
- Department of Hand & Reconstructive Microsurgery, Singapore General Hospital, Singapore, Singapore
| | - Aijie Zhang
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Zhen Qian
- Institute of Intelligent Diagnostics, Beijing United-Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Jiaxing Huang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jia He
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Liu
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
- Beijing Research Institute of Traumatology and Orthopaedics, Beijing, China
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Zhu S, Liu S, Jing X, Li B, Liu H, Yang Y, She C. Evaluation of transformation invariant loss function with distance equilibrium in prediction of imaging photoplethysmography characteristics. Physiol Meas 2024; 45:055004. [PMID: 38604181 DOI: 10.1088/1361-6579/ad3dbf] [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: 08/13/2023] [Accepted: 04/11/2024] [Indexed: 04/13/2024]
Abstract
Objective. Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system. Most researchers employed deep learning IPPG algorithms to extract the blood volume pulse (BVP) signal, analyzing it predominantly through the heart rate (HR). However, this approach often overlooks the inherent intricate time-frequency domain characteristics in the BVP signal, which cannot be comprehensively deduced solely from HR. The analysis of HRV metrics through the BVP signal is imperative. APPROACH In this paper, the transformation invariant loss function with distance equilibrium (TIDLE) loss function is applied to IPPG for the first time, and the details of BVP signal can be recovered better. In detail, TIDLE is tested in four commonly used IPPG deep learning models, which are DeepPhys, EfficientPhys, Physnet and TS_CAN, and compared with other three loss functions, which are mean absolute error (MAE), mean square error (MSE), Neg Pearson Coefficient correlation (NPCC). MAIN RESULTS The experiments demonstrate that MAE and MSE exhibit suboptimal performance in predicting LF/HF across the four models, achieving the Statistic of Mean Absolute Error (MAES) of 25.94% and 34.05%, respectively. In contrast, NPCC and TIDLE yielded more favorable results at 13.51% and 11.35%, respectively. Taking into consideration the morphological characteristics of the BVP signal, on the two optimal models for predicting HRV metrics, namely DeepPhys and TS_CAN, the Pearson coefficients for the BVP signals predicted by TIDLE in comparison to the gold-standard BVP signals achieved values of 0.627 and 0.605, respectively. In contrast, the results based on NPCC were notably lower, at only 0.545 and 0.533, respectively. SIGNIFICANCE This paper contributes significantly to the effective restoration of the morphology and frequency domain characteristics of the BVP signal.
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Affiliation(s)
- Shangwei Zhu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications,People's Republic of China
| | - Shaohua Liu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications,People's Republic of China
| | - Xingjian Jing
- Department of Mechanical Engineering, Hong Kong City University, Hong Kong, People's Republic of China
| | - Bing Li
- The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, People's Republic of China
| | - Hao Liu
- Peking University Shougang Hospital, People's Republic of China
| | - Yuchong Yang
- School of Electronic Engineering, Beijing University of Posts and Telecommunications,People's Republic of China
| | - Chundong She
- School of Electronic Engineering, Beijing University of Posts and Telecommunications,People's Republic of China
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Lazarou E, Exarchos TP. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices. AIMS Neurosci 2024; 11:76-102. [PMID: 38988886 PMCID: PMC11230864 DOI: 10.3934/neuroscience.2024006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/22/2024] [Accepted: 04/08/2024] [Indexed: 07/12/2024] Open
Abstract
Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
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Affiliation(s)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Dept of Informatics, Ionian University, GR49132, Corfu, Greece
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Ontiveros RC, Elgendi M, Missale G, Menon C. Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG. Front Physiol 2023; 14:1296277. [PMID: 38187134 PMCID: PMC10770840 DOI: 10.3389/fphys.2023.1296277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024] Open
Abstract
Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson's correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.
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Affiliation(s)
- Rodrigo Castellano Ontiveros
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, Zurich, Switzerland
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, Zurich, Switzerland
| | - Giuseppe Missale
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, Zurich, Switzerland
- Electronics and Telecommunications Department, Politecnico Di Torino, Torino, Italy
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, Zurich, Switzerland
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Pavlov YG, Gashkova AS, Kasanov D, Kosachenko AI, Kotyusov AI, Kotchoubey B. Task-evoked pulse wave amplitude tracks cognitive load. Sci Rep 2023; 13:22592. [PMID: 38114566 PMCID: PMC10730617 DOI: 10.1038/s41598-023-48917-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023] Open
Abstract
Cognitive load is a crucial factor in mentally demanding activities and holds significance across various research fields. This study aimed to investigate the effectiveness of pulse wave amplitude (PWA) as a measure for tracking cognitive load and associated mental effort in comparison to heart rate (HR) during a digit span task. The data from 78 participants were included in the analyses. Participants performed a memory task in which they were asked to memorize sequences of 5, 9, or 13 digits, and a control task where they passively listened to the sequences. PWA and HR were quantified from photoplethysmography (PPG) and electrocardiography (ECG), respectively. Pupil dilation was also assessed as a measure of cognitive load. We found that PWA showed a strong suppression with increasing memory load, indicating sensitivity to cognitive load. In contrast, HR did not show significant changes with task difficulty. Moreover, when memory load exceeded the capacity of working memory, a reversal of the PWA pattern was observed, indicating cognitive overload. In this respect, changes in PWA in response to cognitive load correlated with the dynamics of pupil dilation, suggesting a potential shared underlying mechanism. Additionally, both HR and PWA demonstrated a relationship with behavioral performance, with higher task-evoked HR and lower PWA associated with better memory performance. Our findings suggest that PWA is a more sensitive measure than HR for tracking cognitive load and overload. PWA, measured through PPG, holds significant potential for practical applications in assessing cognitive load due to its ease of use and sensitivity to cognitive overload. The findings contribute to the understanding of psychophysiological indicators of cognitive load and offer insights into the use of PWA as a non-invasive measure in various contexts.
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Affiliation(s)
- Yuri G Pavlov
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076, Tübingen, Germany.
| | - Anastasia S Gashkova
- Laboratory of Neurotechnology, Ural Federal University, Ekaterinburg, 620000, Russian Federation
| | - Dauren Kasanov
- Laboratory of Neurotechnology, Ural Federal University, Ekaterinburg, 620000, Russian Federation
| | - Alexandra I Kosachenko
- Laboratory of Neurotechnology, Ural Federal University, Ekaterinburg, 620000, Russian Federation
| | - Alexander I Kotyusov
- Laboratory of Neurotechnology, Ural Federal University, Ekaterinburg, 620000, Russian Federation
| | - Boris Kotchoubey
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076, Tübingen, Germany
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Nardelli M, Bailón R. Advances in Wearable Photoplethysmography Applications in Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:7064. [PMID: 37631601 PMCID: PMC10459612 DOI: 10.3390/s23167064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
In the last few years, interest in wearable technology for physiological signal monitoring is rapidly growing, especially during and after the COVID-19 pandemic [...].
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre “E. Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
| | - Raquel Bailón
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, University of Zaragoza, 50015 Zaragoza, Spain;
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
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Kolosov D, Kelefouras V, Kourtessis P, Mporas I. Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware. SENSORS (BASEL, SWITZERLAND) 2023; 23:4550. [PMID: 37177754 PMCID: PMC10181491 DOI: 10.3390/s23094550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/01/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe's BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application's pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.
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Affiliation(s)
- Dimitrios Kolosov
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Vasilios Kelefouras
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK;
| | - Pandelis Kourtessis
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Iosif Mporas
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
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