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Rehman M, Shah RA, Ali NAA, Khan MB, Shah SA, Alomainy A, Hayajneh M, Yang X, Imran MA, Abbasi QH. Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:1251. [PMID: 36772291 PMCID: PMC9919049 DOI: 10.3390/s23031251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
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Affiliation(s)
- Mubashir Rehman
- Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Raza Ali Shah
- Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan
| | - Najah Abed Abu Ali
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates
| | - Muhammad Bilal Khan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates
| | - Syed Aziz Shah
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK
| | - Akram Alomainy
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | - Mohammad Hayajneh
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates
| | - Xiaodong Yang
- School of Electronic Engineering, Xidian University, Xi’an 710071, China
| | | | - Qammer H. Abbasi
- School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
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Rehouma H, Noumeir R, Essouri S, Jouvet P. Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7252. [PMID: 33348827 PMCID: PMC7766256 DOI: 10.3390/s20247252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 01/22/2023]
Abstract
Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.
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Affiliation(s)
- Haythem Rehouma
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Rita Noumeir
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Sandrine Essouri
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
| | - Philippe Jouvet
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
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Jakkaew P, Onoye T. Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6307. [PMID: 33167556 PMCID: PMC7663997 DOI: 10.3390/s20216307] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/22/2020] [Accepted: 11/02/2020] [Indexed: 12/21/2022]
Abstract
Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82±0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.
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Affiliation(s)
- Prasara Jakkaew
- Information Systems Synthesis Laboratory, Department of Information Systems Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan;
- School of Information Technology, Mae Fah Luang University, 333-1 Thasud, Muang, Chiang Rai 57100, Thailand
| | - Takao Onoye
- Information Systems Synthesis Laboratory, Department of Information Systems Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan;
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Continuous Vital Monitoring During Sleep and Light Activity Using Carbon-Black Elastomer Sensors. SENSORS 2020; 20:s20061583. [PMID: 32178307 PMCID: PMC7146453 DOI: 10.3390/s20061583] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/06/2020] [Accepted: 03/10/2020] [Indexed: 11/26/2022]
Abstract
The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed “VitalCore”, a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with “interfacing” material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9–100% accuracy in respiratory peak detection.
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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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Kaur M, Marshall AP, Eastwood-Sutherland C, Salmon BP, Dargaville PA, Gale TJ. Automatic Torso Detection in Images of Preterm Infants. J Med Syst 2017; 41:134. [DOI: 10.1007/s10916-017-0782-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 07/19/2017] [Indexed: 11/25/2022]
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Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor. CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 2016. [DOI: 10.1515/cdbme-2016-0156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
During a magnetic resonance imaging (MRI) exam, a respiratory signal can be required for different purposes, e.g. for patient monitoring, motion compensation or for research studies such as in functional MRI. In addition, respiratory information can be used as a biofeedback for the patient in order to control breath holds or shallow breathing. To reduce patient preparation time or distortions of the MR imaging system, we propose the use of a contactless approach for gathering information about the respiratory activity. An experimental setup based on a commercially available laser range sensor was used to detect respiratory induced motion of the chest or abdomen. This setup was tested using a motion phantom and different human subjects in an MRI scanner. A nasal airflow sensor served as a reference. For both, the phantom as well as the different human subjects, the motion frequency was precisely measured. These results show that a low cost, contactless, laser-based approach can be used to obtain information about the respiratory motion during an MRI exam.
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8
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Li C, Chen F, Qi F, Liu M, Li Z, Liang F, Jing X, Lu G, Wang J. Searching for Survivors through Random Human-Body Movement Outdoors by Continuous-Wave Radar Array. PLoS One 2016; 11:e0152201. [PMID: 27073860 PMCID: PMC4830530 DOI: 10.1371/journal.pone.0152201] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 03/10/2016] [Indexed: 11/18/2022] Open
Abstract
It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor's respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person's head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors.
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Affiliation(s)
- Chuantao Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Fuming Chen
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Fugui Qi
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Miao Liu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Zhao Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Fulai Liang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Xijing Jing
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
| | - Guohua Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- * E-mail: (GL); (JW)
| | - Jianqi Wang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi University of Technology, Hanzhong, China
- * E-mail: (GL); (JW)
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Janssen R, Wang W, Moço A, de Haan G. Video-based respiration monitoring with automatic region of interest detection. Physiol Meas 2015; 37:100-14. [DOI: 10.1088/0967-3334/37/1/100] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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10
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Tawa H, Yonezawa Y, Maki H, Ogawa H, Ninomiya I, Sada K, Hamada S, Caldwell WM. A wireless breathing-training support system for kinesitherapy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:5179-82. [PMID: 19964381 DOI: 10.1109/iembs.2009.5333719] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We have developed a new wireless breathing-training support system for kinesitherapy. The system consists of an optical sensor, an accelerometer, a microcontroller, a Bluetooth module and a laptop computer. The optical sensor, which is attached to the patient's chest, measures chest circumference. The low frequency components of circumference are mainly generated by breathing. The optical sensor outputs the circumference as serial digital data. The accelerometer measures the dynamic acceleration force produced by exercise, such as walking. The microcontroller sequentially samples this force. The acceleration force and chest circumference are sent sequentially via Bluetooth to a physical therapist's laptop computer, which receives and stores the data. The computer simultaneously displays these data so that the physical therapist can monitor the patient's breathing and acceleration waveforms and give instructions to the patient in real time during exercise. Moreover, the system enables a quantitative training evaluation and calculation the volume of air inspired and expired by the lungs.
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Affiliation(s)
- Hiroki Tawa
- Department of Health science, Hiroshima Institute of Technology, Hiroshima 731-5193, Japan.
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