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Lai DKH, Tam AYC, So BPH, Chan ACH, Zha LW, Wong DWC, Cheung JCW. Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM). SENSORS (BASEL, SWITZERLAND) 2024; 24:5016. [PMID: 39124063 PMCID: PMC11314943 DOI: 10.3390/s24155016] [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: 07/03/2024] [Revised: 08/01/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.
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Affiliation(s)
- Derek Ka-Hei Lai
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Andy Yiu-Chau Tam
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Bryan Pak-Hei So
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Andy Chi-Ho Chan
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Li-Wen Zha
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Duo Wai-Chi Wong
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - James Chung-Wai Cheung
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
- Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
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2
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Choo YJ, Lee GW, Moon JS, Chang MC. Noncontact Sensors for Vital Signs Measurement: A Narrative Review. Med Sci Monit 2024; 30:e944913. [PMID: 38961611 PMCID: PMC11302200 DOI: 10.12659/msm.944913] [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: 04/22/2024] [Accepted: 05/26/2024] [Indexed: 07/05/2024] Open
Abstract
Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Gun Woo Lee
- Department of Orthopaedic Surgery, Yeungnam University Hospital, Daegu, South Korea
| | - Jun Sung Moon
- Division of Endocrinology and Metabolism, Yeungnam University Hospital, Deagu, South Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, South Korea
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3
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Zhang J, Hu R, Chen L, Gao Y, Wu DD. Contactless vital signs monitoring in macaques using a mm-wave FMCW radar. Sci Rep 2024; 14:13863. [PMID: 38879652 PMCID: PMC11180203 DOI: 10.1038/s41598-024-63994-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/04/2024] [Indexed: 06/19/2024] Open
Abstract
Heart rate (HR) and respiration rate (RR) play an important role in the study of complex behaviors and their physiological correlations in non-human primates (NHPs). However, collecting HR and RR information is often challenging, involving either invasive implants or tedious behavioral training, and there are currently few established simple and non-invasive techniques for HR and RR measurement in NHPs owing to their stress response or indocility. In this study, we employed a frequency-modulated continuous wave (FMCW) radar to design a novel contactless HR and RR monitoring system. The designed system can estimate HR and RR in real time by placing the FMCW radar on the cage and facing the chest of both awake and anesthetized macaques, the NHP investigated in this study. Experimental results show that the proposed method outperforms existing methods, with averaged absolute errors between the reference monitor and radar estimates of 0.77 beats per minute (bpm) and 1.29 respirations per minute (rpm) for HR and RR, respectively. In summary, we believe that the proposed non-invasive and contactless estimation method could be generalized as a HR and RR monitoring tool for NHPs. Furthermore, after modifying the radar signal-processing algorithms, it also shows promise for applications in other experimental animals for animal welfare, behavioral, neurological, and ethological research.
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Affiliation(s)
- Jiajin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China.
- College of Big Data, Yunnan Agricultural University, Kunming, 650201, China.
| | - Renjie Hu
- College of Big Data, Yunnan Agricultural University, Kunming, 650201, China
| | - Lichang Chen
- College of Big Data, Yunnan Agricultural University, Kunming, 650201, China
| | - Yu Gao
- College of Big Data, Yunnan Agricultural University, Kunming, 650201, China
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China.
- National Resource Center for Non-Human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, Yunnan, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650201, China.
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4
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Fan S, Deng Z. Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System. SENSORS (BASEL, SWITZERLAND) 2024; 24:2058. [PMID: 38610269 PMCID: PMC11014240 DOI: 10.3390/s24072058] [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: 02/29/2024] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.
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Affiliation(s)
| | - Zhenmiao Deng
- School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China;
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Liebetruth M, Kehe K, Steinritz D, Sammito S. Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology. SENSORS (BASEL, SWITZERLAND) 2024; 24:1003. [PMID: 38339721 PMCID: PMC10857015 DOI: 10.3390/s24031003] [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: 12/18/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.
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Affiliation(s)
- Magdalena Liebetruth
- German Air Force Centre of Aerospace Medicine, 51147 Cologne, Germany
- Department of Occupational Medicine, Faculty of Medicine, Otto von Guericke University of Magdeburg, 39120 Magdeburg, Germany
| | - Kai Kehe
- Bundeswehr Medical Service Headquarter, Department A-VI Public Health, 56072 Koblenz, Germany
| | - Dirk Steinritz
- Bundeswehr Institute of Pharmacology and Toxicology, 80937 Munich, Germany
| | - Stefan Sammito
- German Air Force Centre of Aerospace Medicine, 51147 Cologne, Germany
- Department of Occupational Medicine, Faculty of Medicine, Otto von Guericke University of Magdeburg, 39120 Magdeburg, Germany
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6
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Brown MC, Li C. Incorporation of Digital Modulation into Vital Sign Detection and Gesture Recognition Using Multimode Radar Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:7675. [PMID: 37765732 PMCID: PMC10536638 DOI: 10.3390/s23187675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/30/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023]
Abstract
The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have reached a point where available components and technology can support higher speeds than ever before. These advancements present new avenues for radar design, in which digitally controlled phase-modulated continuous wave (PMCW) radar systems can look to support multiple collocated radar systems with low radar-radar interference. This paper proposes a reconfigurable PMCW radar for use in vital sign detection and gesture recognition while utilizing digital carrier modulation and compares the radar responses of various modulation schemes. Binary sequences are used to introduce phase modulation to the carrier wave by use of a field programable gate array (FPGA), allowing for flexibility in the modulation speed and binary sequence. Experimental results from the radar demonstrate the differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection.
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Affiliation(s)
- Michael C Brown
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Changzhi Li
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
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7
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Shi D, Liang F, Qiao J, Wang Y, Zhu Y, Lv H, Yu X, Jiao T, Liao F, Yan K, Wang J, Zhang Y. A Novel Non-Contact Detection and Identification Method for the Post-Disaster Compression State of Injured Individuals Using UWB Bio-Radar. Bioengineering (Basel) 2023; 10:905. [PMID: 37627790 PMCID: PMC10451469 DOI: 10.3390/bioengineering10080905] [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: 06/15/2023] [Revised: 07/22/2023] [Accepted: 07/23/2023] [Indexed: 08/27/2023] Open
Abstract
Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.
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Affiliation(s)
- Ding Shi
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Fulai Liang
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Jiahao Qiao
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Yaru Wang
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Department of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, China; (F.L.); (K.Y.)
| | - Yidan Zhu
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Department of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, China; (F.L.); (K.Y.)
| | - Hao Lv
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Xiao Yu
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Teng Jiao
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Fuyuan Liao
- Department of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, China; (F.L.); (K.Y.)
| | - Keding Yan
- Department of Biomedical Engineering, School of Electronic and Information Engineering, Xi’an Technological University, Xi’an 710032, China; (F.L.); (K.Y.)
| | - Jianqi Wang
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
| | - Yang Zhang
- Department of Medical Electronics, School of Biomedical Engineering, Air Force Medical University, Xi’an 710032, China; (D.S.); (F.L.); (J.Q.); (Y.W.); (Y.Z.); (H.L.); (X.Y.); (T.J.)
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Air Force Medical University, Xi’an 710032, China
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Mauro G, De Carlos Diez M, Ott J, Servadei L, Cuellar MP, Morales-Santos DP. Few-Shot User-Adaptable Radar-Based Breath Signal Sensing. SENSORS (BASEL, SWITZERLAND) 2023; 23:804. [PMID: 36679598 PMCID: PMC9865656 DOI: 10.3390/s23020804] [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: 12/07/2022] [Revised: 01/04/2023] [Accepted: 01/07/2023] [Indexed: 06/17/2023]
Abstract
Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.
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Affiliation(s)
- Gianfranco Mauro
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain
| | | | - Julius Ott
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Lorenzo Servadei
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Manuel P. Cuellar
- Department of Computer Science and Artificial Intelligence, University of Granada, C/. Pdta. Daniel Saucedo Aranda s/n, 18015 Granada, Spain
| | - Diego P. Morales-Santos
- Department of Electronic and Computer Technology, University of Granada, Avenida de Fuente Nueva s/n, 18071 Granada, Spain
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9
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Gao Z, Ali L, Wang C, Liu R, Wang C, Qian C, Sung H, Meng F. Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:7560. [PMID: 36236659 PMCID: PMC9573470 DOI: 10.3390/s22197560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/21/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0-6.3%, the respiration detection was 0-9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.
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Affiliation(s)
- Zhiqiang Gao
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Luqman Ali
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
| | - Cong Wang
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
| | - Ruizhi Liu
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Chunwei Wang
- School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
| | - Cheng Qian
- Ocean College, Zhejiang University, Hangzhou 310027, China
| | - Hokun Sung
- Korea Advanced Nano Fab Center (KANC), Suwon-si 443270, Korea
| | - Fanyi Meng
- School of Information and Communication, Harbin Institute of Technology, Harbin 150001, China
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10
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Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors. SENSORS 2022; 22:s22145249. [PMID: 35890928 PMCID: PMC9321517 DOI: 10.3390/s22145249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 01/25/2023]
Abstract
Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.
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11
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Kim J, Lee WH, Kim SH, Na JY, Lim YH, Cho SH, Cho SH, Park HK. Preclinical trial of noncontact anthropometric measurement using IR-UWB radar. Sci Rep 2022; 12:8174. [PMID: 35581250 PMCID: PMC9112269 DOI: 10.1038/s41598-022-12209-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
Anthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.
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Affiliation(s)
- Jinsup Kim
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Won Hyuk Lee
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Seung Hyun Kim
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Young-Hyo Lim
- Division of Cardiology, Department of Internal Medicine, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Seok Hyun Cho
- Department of Otorhinolaryngology, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Sung Ho Cho
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea.
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12
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Yen HT, Kurosawa M, Kirimoto T, Hakozaki Y, Matsui T, Sun G. A medical radar system for non-contact vital sign monitoring and clinical performance evaluation in hospitalized older patients. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103597] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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13
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Iyer S, Zhao L, Mohan MP, Jimeno J, Siyal MY, Alphones A, Karim MF. mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning. SENSORS 2022; 22:s22093106. [PMID: 35590796 PMCID: PMC9104941 DOI: 10.3390/s22093106] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/25/2022] [Accepted: 04/13/2022] [Indexed: 11/16/2022]
Abstract
A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.
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Affiliation(s)
- Srikrishna Iyer
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.I.); (M.P.M.); (M.Y.S.); (A.A.)
| | - Leo Zhao
- SCALE @ NTU Corp Lab, Nanyang Technological University, Singapore 639798, Singapore; (L.Z.); (J.J.)
- NCS Group, Singapore 469272, Singapore
| | - Manoj Prabhakar Mohan
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.I.); (M.P.M.); (M.Y.S.); (A.A.)
| | - Joe Jimeno
- SCALE @ NTU Corp Lab, Nanyang Technological University, Singapore 639798, Singapore; (L.Z.); (J.J.)
- NCS Group, Singapore 469272, Singapore
| | - Mohammed Yakoob Siyal
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.I.); (M.P.M.); (M.Y.S.); (A.A.)
- SCALE @ NTU Corp Lab, Nanyang Technological University, Singapore 639798, Singapore; (L.Z.); (J.J.)
| | - Arokiaswami Alphones
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.I.); (M.P.M.); (M.Y.S.); (A.A.)
- SCALE @ NTU Corp Lab, Nanyang Technological University, Singapore 639798, Singapore; (L.Z.); (J.J.)
| | - Muhammad Faeyz Karim
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.I.); (M.P.M.); (M.Y.S.); (A.A.)
- SCALE @ NTU Corp Lab, Nanyang Technological University, Singapore 639798, Singapore; (L.Z.); (J.J.)
- Correspondence:
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14
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Xu H, Ebrahim MP, Hasan K, Heydari F, Howley P, Yuce MR. Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors. SENSORS 2021; 22:s22010083. [PMID: 35009628 PMCID: PMC8747437 DOI: 10.3390/s22010083] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 12/01/2022]
Abstract
Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.
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Affiliation(s)
- Hongqiang Xu
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia; (H.X.); (M.P.E.); (K.H.); (F.H.)
| | - Malikeh P. Ebrahim
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia; (H.X.); (M.P.E.); (K.H.); (F.H.)
| | - Kareeb Hasan
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia; (H.X.); (M.P.E.); (K.H.); (F.H.)
| | - Fatemeh Heydari
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia; (H.X.); (M.P.E.); (K.H.); (F.H.)
| | - Paul Howley
- Planet Innovation, Box Hill, VIC 3128, Australia;
| | - Mehmet Rasit Yuce
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia; (H.X.); (M.P.E.); (K.H.); (F.H.)
- Correspondence: ; Tel.: +61-3-990-53932
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15
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Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars. SENSORS 2021; 21:s21165503. [PMID: 34450945 PMCID: PMC8402280 DOI: 10.3390/s21165503] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/16/2022]
Abstract
Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest-abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.
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16
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Through-Wall Multi-Subject Localization and Vital Signs Monitoring Using UWB MIMO Imaging Radar. REMOTE SENSING 2021. [DOI: 10.3390/rs13152905] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Radar-based non-contact vital signs monitoring has great value in through-wall detection applications. This paper presents the theoretical and experimental study of through-wall respiration and heartbeat pattern extraction from multiple subjects. To detect the vital signs of multiple subjects, we employ a low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) imaging radar and derive the relationship between radar images and vibrations caused by human cardiopulmonary movements. The derivation indicates that MIMO radar imaging with the stepped-frequency continuous-wave (SFCW) improves the signal-to-noise ratio (SNR) critically by the factor of radar channel number times frequency number compared with continuous-wave (CW) Doppler radars. We also apply the three-dimensional (3-D) higher-order cumulant (HOC) to locate multiple subjects and extract the phase sequence of the radar images as the vital signs signal. To monitor the cardiopulmonary activities, we further exploit the VMD algorithm with a proposed grouping criterion to adaptively separate the respiration and heartbeat patterns. A series of experiments have validated the localization and detection of multiple subjects behind a wall. The VMD algorithm is suitable for separating the weaker heartbeat pattern from the stronger respiration pattern by the grouping criterion. Moreover, the continuous monitoring of heart rate (HR) by the MIMO radar in real scenarios shows a strong consistency with the reference electrocardiogram (ECG).
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Kim BS, Jin Y, Lee J, Kim S. High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation. SENSORS 2021; 21:s21124018. [PMID: 34200856 PMCID: PMC8230523 DOI: 10.3390/s21124018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 11/16/2022]
Abstract
This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of 88% compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.
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Affiliation(s)
- Bong-seok Kim
- Division of Automotive Technology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; (B.-s.K.); (Y.J.); (J.L.)
| | - Youngseok Jin
- Division of Automotive Technology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; (B.-s.K.); (Y.J.); (J.L.)
| | - Jonghun Lee
- Division of Automotive Technology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; (B.-s.K.); (Y.J.); (J.L.)
- Department of Interdisciplinary Engineering, DGIST, Daegu 42988, Korea
| | - Sangdong Kim
- Division of Automotive Technology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea; (B.-s.K.); (Y.J.); (J.L.)
- Department of Interdisciplinary Engineering, DGIST, Daegu 42988, Korea
- Correspondence: ; Tel.: +82-53-785-4561
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18
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de Goederen R, Pu S, Silos Viu M, Doan D, Overeem S, Serdijn WA, Joosten KFM, Long X, Dudink J. Radar-based sleep stage classification in children undergoing polysomnography: a pilot-study. Sleep Med 2021; 82:1-8. [PMID: 33866298 DOI: 10.1016/j.sleep.2021.03.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/18/2021] [Accepted: 03/20/2021] [Indexed: 10/21/2022]
Abstract
STUDY OBJECTIVES Unobtrusive monitoring of sleep and sleep disorders in children presents challenges. We investigated the possibility of using Ultra-Wide band (UWB) radar to measure sleep in children. METHODS Thirty-two children scheduled to undergo a clinical polysomnography participated; their ages ranged from 2 months to 14 years. During the polysomnography, the children's body movements and breathing rate were measured by an UWB-radar. A total of 38 features were calculated from the motion signals and breathing rate obtained from the raw radar signals. Adaptive boosting was used as machine learning classifier to estimate sleep stages, with polysomnography as gold standard method for comparison. RESULTS Data of all participants combined, this study achieved a Cohen's Kappa coefficient of 0.67 and an overall accuracy of 89.8% for wake and sleep classification, a Kappa of 0.47 and an accuracy of 72.9% for wake, rapid-eye-movement (REM) sleep, and non-REM sleep classification, and a Kappa of 0.43 and an accuracy of 58.0% for wake, REM sleep, light sleep and deep sleep classification. CONCLUSION Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children.
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Affiliation(s)
- R de Goederen
- Pediatric Intensive Care Unit, Erasmus MC, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht Utrecht, the Netherlands
| | - S Pu
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
| | - M Silos Viu
- Section Bioelectronics, Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
| | - D Doan
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht Utrecht, the Netherlands
| | - S Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
| | - W A Serdijn
- Section Bioelectronics, Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
| | - K F M Joosten
- Pediatric Intensive Care Unit, Erasmus MC, Sophia Children's Hospital, Rotterdam, the Netherlands
| | - X Long
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
| | - J Dudink
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht Utrecht, the Netherlands.
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