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Hao Z, Wang Y, Li F, Ding G, Gao Y. mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar. SENSORS (BASEL, SWITZERLAND) 2024; 24:4315. [PMID: 39001094 PMCID: PMC11243972 DOI: 10.3390/s24134315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/11/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024]
Abstract
Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.
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Affiliation(s)
- Zhanjun Hao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
- Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China
| | - Yue Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Fenfang Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Guozhen Ding
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
| | - Yifei Gao
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; (Y.W.); (F.L.); (G.D.); (Y.G.)
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2
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Choo YJ, Lee GW, Moon JS, Chang MC. Application of non-contact sensors for health monitoring in hospitals: a narrative review. Front Med (Lausanne) 2024; 11:1421901. [PMID: 38933102 PMCID: PMC11199382 DOI: 10.3389/fmed.2024.1421901] [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: 04/23/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The continuous monitoring of the health status of patients is essential for the effective monitoring of disease progression and the management of symptoms. Recently, health monitoring using non-contact sensors has gained interest. Therefore, this study aimed to investigate the use of non-contact sensors for health monitoring in hospital settings and evaluate their potential clinical applications. A comprehensive literature search was conducted using PubMed to identify relevant studies published up to February 26, 2024. The search terms included "hospital," "monitoring," "sensor," and "non-contact." Studies that used non-contact sensors to monitor health status in hospital settings were included in this review. Of the 38 search results, five studies met the inclusion criteria. The non-contact sensors described in the studies were radar, infrared, and microwave sensors. These non-contact sensors were used to obtain vital signs, such as respiratory rate, heart rate, and body temperature, and were then compared with the results from conventional measurement methods (polysomnography, nursing records, and electrocardiography). In all the included studies, non-contact sensors demonstrated a performance similar to that of conventional health-related parameter measurement methods. Non-contact sensors are expected to be a promising solution for health monitoring in hospital settings.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Gun Woo Lee
- Department of Orthopaedic Surgery, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Jun Sung Moon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, Republic of Korea
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Huang S, Dai H, Yu X, Wu X, Wang K, Hu J, Yao H, Huang R, Niu W. A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network. iScience 2024; 27:109093. [PMID: 38375238 PMCID: PMC10875158 DOI: 10.1016/j.isci.2024.109093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 12/09/2023] [Accepted: 01/30/2024] [Indexed: 02/21/2024] Open
Abstract
The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.
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Affiliation(s)
- Shangjun Huang
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
| | - Houde Dai
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Xiaoming Yu
- Rehabilitation Medical Center, Shanghai Seventh’s Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200137, China
| | - Xie Wu
- Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, Shanghai 200438, China
| | - Kuan Wang
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
| | - Jiaxin Hu
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Hanchen Yao
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Rui Huang
- Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, Shanghai 200438, China
| | - Wenxin Niu
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
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Goldfine CE, Oshim MFT, Chapman BP, Ganesan D, Rahman T, Carreiro SP. Contactless Monitoring System Versus Gold Standard for Respiratory Rate Monitoring in Emergency Department Patients: Pilot Comparison Study. JMIR Form Res 2024; 8:e44717. [PMID: 38363588 PMCID: PMC10907933 DOI: 10.2196/44717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Respiratory rate is a crucial indicator of disease severity yet is the most neglected vital sign. Subtle changes in respiratory rate may be the first sign of clinical deterioration in a variety of disease states. Current methods of respiratory rate monitoring are labor-intensive and sensitive to motion artifacts, which often leads to inaccurate readings or underreporting; therefore, new methods of respiratory monitoring are needed. The PulsON 440 (P440; TSDR Ultra Wideband Radios and Radars) radar module is a contactless sensor that uses an ultrawideband impulse radar to detect respiratory rate. It has previously demonstrated accuracy in a laboratory setting and may be a useful alternative for contactless respiratory monitoring in clinical settings; however, it has not yet been validated in a clinical setting. OBJECTIVE The goal of this study was to (1) compare the P440 radar module to gold standard manual respiratory rate monitoring and standard of care telemetry respiratory monitoring through transthoracic impedance plethysmography and (2) compare the P440 radar to gold standard measurements of respiratory rate in subgroups based on sex and disease state. METHODS This was a pilot study of adults aged 18 years or older being monitored in the emergency department. Participants were monitored with the P440 radar module for 2 hours and had gold standard (manual respiratory counting) and standard of care (telemetry) respiratory rates recorded at 15-minute intervals during that time. Respiratory rates between the P440, gold standard, and standard telemetry were compared using Bland-Altman plots and intraclass correlation coefficients. RESULTS A total of 14 participants were enrolled in the study. The P440 and gold standard Bland-Altman analysis showed a bias of -0.76 (-11.16 to 9.65) and an intraclass correlation coefficient of 0.38 (95% CI 0.06-0.60). The P440 and gold standard had the best agreement at normal physiologic respiratory rates. There was no change in agreement between the P440 and the gold standard when grouped by admitting diagnosis or sex. CONCLUSIONS Although the P440 did not have statistically significant agreement with gold standard respiratory rate monitoring, it did show a trend of increased agreement in the normal physiologic range, overestimating at low respiratory rates, and underestimating at high respiratory rates. This trend is important for adjusting future models to be able to accurately detect respiratory rates. Once validated, the contactless respiratory monitor provides a unique solution for monitoring patients in a variety of settings.
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Affiliation(s)
- Charlotte E Goldfine
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Md Farhan Tasnim Oshim
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States
| | - Brittany P Chapman
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Deepak Ganesan
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States
| | - Tauhidur Rahman
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, United States
| | - Stephanie P Carreiro
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Bachir W, Ismael FS, Alaineya NHA. Laser spectroscopic method for remote sensing of respiratory rate. Phys Eng Sci Med 2023; 46:1249-1258. [PMID: 37358781 PMCID: PMC10480269 DOI: 10.1007/s13246-023-01292-x] [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: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
Noncontact sensing methods for measuring vital signs have recently gained interest, particularly for long-term monitoring. This study introduces a new method for measuring respiratory rate remotely. The proposed method is based on the reflection of a laser beam off a striped card attached to a moving platform simulating chest wall displacements. A wide range of frequencies (n = 35) from 0.06 to 2.2 Hz corresponding to both normal and pathological human respiratory rates were simulated using a moving mechanical platform. Reflected spectra (n = 105) were collected by a spectrometer in a dynamic mode. Fourier analysis was performed to retrieve the breathing frequency. The results show a striking agreement between measurements and reference frequencies. The results also show that low frequencies corresponding to respiratory rates can be detected with high accuracy (uncertainty is well below 5%). A validation test of the measuring method on a human subject demonstrated a great potential for remote respiration rate monitoring of adults and neonates in a clinical environment.
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Affiliation(s)
- Wesam Bachir
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Św. A. Boboli 8 St, 02-525, Warsaw, Poland.
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria.
| | - Fatimah Samie Ismael
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria
| | - Nour Hasan Arry Alaineya
- Biomedical Photonics Laboratory, Higher Institute for Laser Research and Applications, Damascus University, Damascus, Syria
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Boiko A, Martínez Madrid N, Seepold R. Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115038. [PMID: 37299762 DOI: 10.3390/s23115038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
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Affiliation(s)
- Andrei Boiko
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
| | - Natividad Martínez Madrid
- Internet of Things Laboratory, School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
| | - Ralf Seepold
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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7
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Liang WM, Bai ZM, Aihemaiti M, Yuan L, Hong ZM, Xiao J, Ren FF, Rukšėnas O. Women's Respiratory Movements during Spontaneous Breathing and Physical Fitness: A Cross-Sectional, Correlational Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12007. [PMID: 36231308 PMCID: PMC9566329 DOI: 10.3390/ijerph191912007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Abdominal/diaphragmatic breathing exercises are popular worldwide and have been proven to be beneficial for physical performance. Is abdominal motion (AM) during spontaneous breathing correlated with physical fitness? The present study aimed to answer this question. METHODS 434 women (aged 20-59) were enrolled and participated in respiration tests using two respiration belts (one was tied at the height of the xiphoid and another at the navel) to detect AM and thoracic motion (TM). They also performed physical fitness tests to measure body size, muscular strength, muscular power, muscular endurance, balance, flexibility, reaction time, and cardiorespiratory endurance. RESULTS All the correlation coefficients between respiratory movements (AM, TM, AM + TM, AM/(AM + TM)) and physical fitness outcomes were less than 0.4/-0.4. Only AM and muscular power (countermovement jump height) had a weak correlation, with a correlation coefficient close to 0.4 in the 20-29-year age group (rs = 0.398, p = 0.011, n = 40). CONCLUSIONS Women's respiratory movements during spontaneous breathing were not correlated with physical fitness. Future studies may focus on the relationship between AM and countermovement jump height in young women with a larger sample size and using ultrasound to directly test the excursion of the diaphragm.
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Affiliation(s)
- Wen-Ming Liang
- Life Sciences Center, Vilnius University, LT-10257 Vilnius, Lithuania
- Department of Physiotherapy and Rehabilitation, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Zhen-Min Bai
- School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China
| | - Maiwulamu Aihemaiti
- School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China
| | - Lei Yuan
- School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China
| | - Zhi-Min Hong
- School of Science, Inner Mongolia University of Technology, Hohhot 010051, China
| | - Jing Xiao
- Department of Physiotherapy and Rehabilitation, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Fei-Fei Ren
- Department of Physical Education, Beijing Language and Culture University, Beijing 100083, China
| | - Osvaldas Rukšėnas
- Life Sciences Center, Vilnius University, LT-10257 Vilnius, Lithuania
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Fallatah A, Bolic M, MacPherson M, La Russa DJ. Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar. SENSORS 2022; 22:s22062287. [PMID: 35336458 PMCID: PMC8954556 DOI: 10.3390/s22062287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/11/2022] [Accepted: 02/25/2022] [Indexed: 12/21/2022]
Abstract
The goal of this paper is to evaluate the potential of a low-cost, ultra-wideband radar system for detecting and monitoring respiratory motion during radiation therapy treatment delivery. Radar signals from breathing motion patterns simulated using a respiratory motion phantom were captured during volumetric modulated arc therapy (VMAT) delivery. Gantry motion causes strong interference affecting the quality of the extracted respiration motion signal. We developed an artificial neural network (ANN) model for recovering the breathing motion patterns. Next, automated classification into four classes of breathing amplitudes is performed, including no breathing, breath hold, free breathing and deep inspiration. Breathing motion patterns extracted from the radar signal are in excellent agreement with the reference data recorded by the respiratory motion phantom. The classification accuracy of simulated deep inspiration breath hold breathing was 94% under the worst case interference from gantry motion and linac operation. Ultra-wideband radar systems can achieve accurate breathing rate estimation in real-time during dynamic radiation delivery. This technology serves as a viable alternative to motion detection and respiratory gating systems based on surface detection, and is well-suited to dynamic radiation treatment techniques. Novelties of this work include detection of the breathing signal using radar during strong interference from simultaneous gantry motion, and using ANN to perform adaptive signal processing to recover breathing signal from large interference signals in real time.
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Affiliation(s)
- Anwar Fallatah
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
| | - Miodrag Bolic
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
- Correspondence:
| | - Miller MacPherson
- Department of Radiology, Division of Medical Physics, Faculty of Medicine, University of Ottawa, 501 Smyth Road, Box 232, Ottawa, ON K1H 8L6, Canada;
- The Ottawa Hospital Research Institute, 501 Smyth Road, Box 511, Ottawa, ON K1H 8L6, Canada
- Radiation Medicine Program, The Ottawa Hospital, 501 Smyth Road, Box 927, Ottawa, ON K1H 8L6, Canada;
- Department of Physics, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
| | - Daniel J. La Russa
- Radiation Medicine Program, The Ottawa Hospital, 501 Smyth Road, Box 927, Ottawa, ON K1H 8L6, Canada;
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