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Khanam FTZ, Perera AG, Al-Naji A, Mcintyre TD, Chahl J. Integrating RGB-thermal image sensors for non-contact automatic respiration rate monitoring. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2024; 41:1140-1151. [PMID: 38856428 DOI: 10.1364/josaa.520757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/23/2024] [Indexed: 06/11/2024]
Abstract
Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.
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Han X, Zhai Q, Zhang N, Zhang X, He L, Pan M, Zhang B, Liu T. A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:6681. [PMID: 37571465 PMCID: PMC10422594 DOI: 10.3390/s23156681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/17/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023]
Abstract
Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.
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Affiliation(s)
- Xiangyu Han
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (X.H.); (Q.Z.)
| | - Qian Zhai
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (X.H.); (Q.Z.)
| | - Ning Zhang
- National Research Center for Rehabilitation Technical Aids, Beijing 100176, China; (N.Z.); (X.Z.)
| | - Xiufeng Zhang
- National Research Center for Rehabilitation Technical Aids, Beijing 100176, China; (N.Z.); (X.Z.)
| | - Long He
- Zhiyuan Research Institute, Hangzhou 310024, China;
| | - Min Pan
- Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK;
| | - Bin Zhang
- Department of Electrical Engineering, University of South Carolina, Columbia, SC 29208, USA;
| | - Tao Liu
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; (X.H.); (Q.Z.)
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A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10020607] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for the automatic strategy is 3.28 ± 3.33 % with and agreement with respect of the reference of ≈98%.
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A Pilot Study for Estimating the Cardiopulmonary Signals of Diverse Exotic Animals Using a Digital Camera. SENSORS 2019; 19:s19245445. [PMID: 31835550 PMCID: PMC6960731 DOI: 10.3390/s19245445] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/01/2019] [Accepted: 12/09/2019] [Indexed: 11/17/2022]
Abstract
Monitoring the cardiopulmonary signal of animals is a challenge for veterinarians in conditions when contact with a conscious animal is inconvenient, difficult, damaging, distressing or dangerous to personnel or the animal subject. In this pilot study, we demonstrate a computer vision-based system and use examples of exotic, untamed species to demonstrate this means to extract the cardiopulmonary signal. Subject animals included the following species: Giant panda (Ailuropoda melanoleuca), African lions (Panthera leo), Sumatran tiger (Panthera tigris sumatrae), koala (Phascolarctos cinereus), red kangaroo (Macropus rufus), alpaca (Vicugna pacos), little blue penguin (Eudyptula minor), Sumatran orangutan (Pongo abelii) and Hamadryas baboon (Papio hamadryas). The study was done without need for restriction, fixation, contact or disruption of the daily routine of the subjects. The pilot system extracts the signal from the abdominal-thoracic region, where cardiopulmonary activity is most likely to be visible using image sequences captured by a digital camera. The results show motion on the body surface of the subjects that is characteristic of cardiopulmonary activity and is likely to be useful to estimate physiological parameters (pulse rate and breathing rate) of animals without any physical contact. The results of the study suggest that a fully controlled study against conventional physiological monitoring equipment is ethically warranted, which may lead to a novel approach to non-contact physiological monitoring and remotely sensed health assessment of animals. The method shows promise for applications in veterinary practice, conservation and game management, animal welfare and zoological and behavioral studies.
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Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study. Pediatr Res 2019; 86:738-741. [PMID: 31351437 DOI: 10.1038/s41390-019-0506-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 07/04/2019] [Accepted: 07/11/2019] [Indexed: 11/08/2022]
Abstract
BACKGROUND Non-contact heart rate (HR) and respiratory rate (RR) monitoring is necessary for preterm infants due to the potential for the adhesive electrodes of conventional electrocardiogram (ECG) to cause damage to the epidermis. This study was performed to evaluate the agreement between HR and RR measurements of preterm infants using a non-contact computer vision system with comparison to measurements obtained by the ECG. METHODS A single-centre, cross-sectional observational study was conducted in a Neonatal Unit. Ten infants and their ECG monitors were videoed using two Nikon cameras for 10 min. HR and RR measurements obtained from the non-contact system were extracted using advanced signal processing techniques and later compared to the ECG readings using Bland-Altman analysis. RESULTS The non-contact system was able to detect an apnoea when the ECG determined movement as respirations. Although the mean bias between both methods was relatively low, the limits of agreement for HR were -8.3 to 17.4 beats per minute (b.p.m.) and for RR, -22 to 23.6 respirations per minute (r.p.m.). CONCLUSIONS This study provides necessary data for improving algorithms to address confounding variables common to the neonatal population. Further studies investigating the robustness of the proposed system for premature infants are therefore required.
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Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9204474] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.
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Iozza L, Lázaro J, Cerina L, Silvestri D, Mainardi L, Laguna P, Gil E. Monitoring breathing rate by fusing the physiological impact of respiration on video-photoplethysmogram with head movements. Physiol Meas 2019; 40:094002. [DOI: 10.1088/1361-6579/ab4102] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Gibson K, Al-Naji A, Fleet JA, Steen M, Chahl J, Huynh J, Morris S. Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study. JMIR Res Protoc 2019; 8:e13400. [PMID: 31469077 PMCID: PMC6786848 DOI: 10.2196/13400] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/12/2019] [Accepted: 05/25/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. OBJECTIVE The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. METHODS A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. RESULTS Study enrolment began in May 2018. Results of this study were published in July 2019. CONCLUSIONS The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/13400.
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Affiliation(s)
- Kim Gibson
- School of Nursing and Midwifery, University of South Australia, Adelaide, Australia
| | - Ali Al-Naji
- Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
| | - Julie-Anne Fleet
- School of Nursing and Midwifery, University of South Australia, Adelaide, Australia
| | - Mary Steen
- School of Nursing and Midwifery, University of South Australia, Adelaide, Australia
| | - Javaan Chahl
- School of Engineering, University of South Australia, Adelaide, Australia
| | - Jasmine Huynh
- School of Engineering, University of South Australia, Adelaide, Australia
| | - Scott Morris
- College of Medicine and Public Health, Flinders University, Adelaide, Australia.,Neonatal Unit, Flinders Medical Centre, Adelaide, Australia
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Harford M, Catherall J, Gerry S, Young JD, Watkinson P. Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review. Physiol Meas 2019; 40:06TR01. [PMID: 31051494 DOI: 10.1088/1361-6579/ab1f1d] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Over the last 15 years, developments in camera technology have coincided with increased availability and affordability. This has led to an increasing interest in using these technologies in healthcare settings. Image-based monitoring methods potentially allow multiple vital signs to be measured concurrently using a non-contact sensor. We have undertaken a systematic review of the current availability and performance of these monitoring methods. APPROACH A multiple database search was conducted using MEDLINE, Embase, CINAHL, Cochrane Library, OpenGrey, IEEE Xplore Library and ACM Digital Library to July 2018. We included studies comparing image-based heart rate, respiratory rate, oxygen saturation and blood pressure monitoring methods against one or more validated reference device(s). Each included study was assessed using the modified GRRAS criteria for reporting bias. MAIN RESULTS Of 30 279 identified studies, 161 were included in the final analysis. Twenty studies (20/161, 12%) were carried out on patients in clinical settings, while the remainder were conducted in academic settings using healthy volunteer populations. The 18-40 age group was best represented across the identified studies. One hundred and twenty studies (120/161, 75%) estimated heart rate, followed by 62 studies (62/161, 39%) estimating respiratory rate. Fewer studies focused on oxygen saturation (11/161, 7%) or blood pressure (6/161, 4%) estimation. Fifty-one heart rate studies (51/120, 43%) and 24 respiratory rate studies (24/62, 39%) used Bland-Altman analysis to report their results. Of the heart rate studies, 28 studies (28/51, 55%) showed agreement within industry standards of [Formula: see text]5 beats per minute. Only two studies achieved this within clinical settings. Of the respiratory rate studies, 13 studies (13/24, 54%) showed agreement within industry standards of [Formula: see text]3 breaths per minute, but only one study achieved this in a clinical setting. Statistical analysis was heterogeneous across studies with frequent inappropriate use of correlation. The majority of studies (99/161, 61%) monitored subjects for under 5 min. Three studies (3/161, 2%) monitored subjects for over 60 min, all of which were conducted in hospital settings. SIGNIFICANCE Heart rate and respiratory rate monitoring using video images is currently possible and performs within clinically acceptable limits under experimental conditions. Camera-derived estimates were less accurate in the proportion of studies conducted in clinical settings. We would encourage thorough reporting of the population studied, details of clinically relevant aspects of methodology, and the use of appropriate statistical methods in future studies. Systematic review registration: PROSPERO CRD42016029167 Protocol: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-017-0615-3.
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Affiliation(s)
- M Harford
- Kadoorie Centre for Critical Care Research and Education, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2019. [DOI: 10.3390/jsan8020032] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.
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Al-Naji A, Perera AG, Chahl J. Remote measurement of cardiopulmonary signal using an unmanned aerial vehicle. ACTA ACUST UNITED AC 2018. [DOI: 10.1088/1757-899x/405/1/012001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR. Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1802. [PMID: 29865289 PMCID: PMC6022164 DOI: 10.3390/s18061802] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 05/27/2018] [Accepted: 05/27/2018] [Indexed: 11/24/2022]
Abstract
Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks). To validate the proposed method and ML models, a home oscillometric monitor and a finger sensor were used. Results showed high correlations with the G color channel (R² = 0.83). Two ML models were developed using three face-regions: (i) Model 1 to predict HR and BP using the RVA outputs with R = 0.85 and (ii) Model 2 based on time-series prediction with HR, magnitude and luminosity from RVA inputs to HR values every second with R = 0.97. An application for the sensory analysis of chocolate showed significant correlations between changes in HR and BP with chocolate hardness and purchase intention.
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Affiliation(s)
- Claudia Gonzalez Viejo
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Sigfredo Fuentes
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Damir D Torrico
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
| | - Frank R Dunshea
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
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Al-Naji A, Chahl J. Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2017; 5:1900510. [PMID: 29043113 PMCID: PMC5642312 DOI: 10.1109/jtehm.2017.2757485] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 09/20/2017] [Accepted: 09/22/2017] [Indexed: 11/09/2022]
Abstract
Most existing non-contact monitoring systems are limited to detecting physiological signs from a single subject at a time. Still, another challenge facing these systems is that they are prone to noise artifacts resulting from motion of subjects, facial expressions, talking, skin tone, and illumination variations. This paper proposes an efficient non-contact system based on a digital camera to track the cardiorespiratory signal from a number of subjects (up to six persons) at the same time with a new method for noise artifact removal. The proposed system relied on the physiological and physical effects as a result of the activity of the cardiovascular and respiratory systems, such as skin color changes and head motion. Since these effects are imperceptible to the human eye and highly affected by the noise variations, we used advanced signal and video processing techniques, including developing video magnification technique, complete ensemble empirical mode decomposition with adaptive noise, and canonical correlation analysis to extract the heart rate and respiratory rate from multiple subjects under the noise artifact assumptions. The experimental results of the proposed system had a significant correlation (Pearson's correlation coefficient = 0.9994, Spearman correlation coefficient = 0.9987, and root mean square error = 0.32) when compared with the conventional contact methods (pulse oximeter and piezorespiratory belt), which makes the proposed system a promising candidate for novel applications.
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Affiliation(s)
- Ali Al-Naji
- School of EngineeringUniversity of South AustraliaMawson LakesSA5095Australia
- Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10022Iraq
| | - Javaan Chahl
- School of EngineeringUniversity of South AustraliaMawson LakesSA5095Australia
- Joint and Operations Analysis DivisionDefence Science and Technology GroupMelbourneVIC3207Australia
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