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Choo YJ, Moon JS, Lee GW, Park WT, Won H, Chang MC. Application of noncontact sensors for cardiopulmonary physiology and body weight monitoring at home: A narrative review. Medicine (Baltimore) 2024; 103:e39607. [PMID: 39252250 PMCID: PMC11383488 DOI: 10.1097/md.0000000000039607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
Monitoring health status at home has garnered increasing interest. Therefore, this study investigated the potential feasibility of using noncontact sensors in actual home settings. We searched PubMed for relevant studies published until February 19, 2024, using the keywords "home-based," "home," "monitoring," "sensor," and "noncontact." The studies included in this review involved the installation of noncontact sensors in actual home settings and the evaluation of their performance for health status monitoring. Among the 3 included studies, 2 monitored respiratory status during sleep and 1 monitored body weight and cardiopulmonary physiology. Measurements such as heart rate, respiratory rate, and body weight obtained with noncontact sensors were compared with the results obtained from polysomnography, polygraphy, and commercial scales. All included studies demonstrated that noncontact sensors produced results comparable to those of standard measurement tools, confirming their excellent capability for biometric measurements. Overall, noncontact sensors have sufficient potential for monitoring health status at home.
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Affiliation(s)
- Yoo Jin Choo
- Department of Physical Medicine and Rehabilitation, 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
| | - Gun Woo Lee
- Department of Orthopaedic Surgery, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Wook-Tae Park
- Department of Orthopaedic Surgery, College of Medicine, Yeungnam University, Daegu, Republic of Korea
| | - Heeyeon Won
- Regional Leading Research Center on Development of Multimodal Untact Sensing for Life-Logging, Yeungnam University Industry-Academic Cooperation Foundation, Gyeongsan-si, Gyeongsangbuk-do, 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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Lin SY, Tsai CY, Majumdar A, Ho YH, Huang YW, Kao CK, Yeh SM, Hsu WH, Kuan YC, Lee KY, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med 2024; 20:1267-1277. [PMID: 38546033 PMCID: PMC11294131 DOI: 10.5664/jcsm.11136] [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: 12/19/2023] [Revised: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 08/03/2024]
Abstract
STUDY OBJECTIVES The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.
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Affiliation(s)
- Shang-Yang Lin
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Yu-Hsuan Ho
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Yu-Wen Huang
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Chun-Kai Kao
- Wireless Technology and Antenna Research and Development Department, Wistron Corporation, Taipei, Taiwan
| | - Shang-Min Yeh
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Po-Hao Feng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Jiunn-Horng Kang
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
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Gross-Isselmann JA, Eggert T, Wildenauer A, Dietz-Terjung S, Grosse Sundrup M, Schoebel C. Validation of the Sleepiz One + as a radar-based sensor for contactless diagnosis of sleep apnea. Sleep Breath 2024; 28:1691-1699. [PMID: 38744804 PMCID: PMC11303430 DOI: 10.1007/s11325-024-03057-6] [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: 02/15/2024] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI). METHODS From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring. RESULTS The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was rp = 0.94 and rp = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%. CONCLUSION The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09.
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Affiliation(s)
| | - Torsten Eggert
- Devision of Sleep & Telemedicine, Ruhrlandklinik, University Medicine Essen, University of Duisburg-Essen, Essen, Germany
| | - Alina Wildenauer
- Devision of Sleep & Telemedicine, Ruhrlandklinik, University Medicine Essen, University of Duisburg-Essen, Essen, Germany
| | - Sarah Dietz-Terjung
- Devision of Sleep & Telemedicine, Ruhrlandklinik, University Medicine Essen, University of Duisburg-Essen, Essen, Germany
| | - Martina Grosse Sundrup
- Devision of Sleep & Telemedicine, Ruhrlandklinik, University Medicine Essen, University of Duisburg-Essen, Essen, Germany
| | - Christoph Schoebel
- Devision of Sleep & Telemedicine, Ruhrlandklinik, University Medicine Essen, University of Duisburg-Essen, Essen, Germany
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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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Tschopp S, Borner U, Caversaccio M, Tschopp K. Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study. J Clin Sleep Med 2024; 20:1079-1086. [PMID: 38415722 PMCID: PMC11217624 DOI: 10.5664/jcsm.11070] [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: 12/03/2023] [Revised: 02/04/2024] [Accepted: 02/07/2024] [Indexed: 02/29/2024]
Abstract
STUDY OBJECTIVES Night-to-night variability of sleep-disordered breathing limits the diagnostic accuracy of a single measurement. Multiple recordings using a reliable, affordable method could reduce the uncertainty and avoid misdiagnosis, which could be possible with radar-based home sleep apnea testing (HSAT). METHODS We recruited consecutive patients with suspected sleep-disordered breathing and performed contactless radar-based HSAT with automated scoring (Sleepiz One; Sleepiz AG, Zurich, Switzerland) over 10 nights. During the first night, patients were simultaneously measured with peripheral arterial tonometry. RESULTS Twenty-four of the 28 included patients could achieve a minimum of 4 measurements. The failure rate was 16% (37 of 238 measurements). The apnea-hypopnea index (AHI) and oxygen desaturation index were consistently lower with radar-based HSAT compared with peripheral arterial tonometry. The variability of the AHI was considerable, with a standard error of measurement of 5.2 events/h (95% confidence interval [CI]: 4.6-5.7 events/h) and a minimal detectable difference of 14.4 events/h (95% CI: 12.7-15.9 events/h). Alcohol consumption partially accounted for the variability, with an AHI increase of 1.7 events/h (95% CI: 0.6-2.8 events/h) for each standard drink. Based on a single measurement, 17% of patients were misdiagnosed and 32% were misclassified for sleep-disordered breathing severity. After 5 measurements, the mean AHI of the measured nights stabilized with no evidence of substantial changes with additional measurements. CONCLUSIONS Night-to-night variability is considerable and stable over 10 nights. HSAT using radar-based methods over multiple nights is feasible and well tolerated by patients. It could offer lower costs and allow for multiple-night testing to increase accuracy. However, validation and reducing the failure rate are necessary for implementation in the clinical routine. CLINICAL TRIAL REGISTRATION Registry: ClinicalTrials.gov; Name: Recording of Multiple Nights Using a New Contactless Device (Sleepiz One Connect) in Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/study/NCT05134402; Identifier: NCT05134402. CITATION Tschopp S, Borner U, Caversaccio M, Tschopp K. Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study. J Clin Sleep Med. 2024;20(7):1079-1086.
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Affiliation(s)
- Samuel Tschopp
- Department of Otorhinolaryngology, Head and Neck Surgery, Inselspital, University Hospital and University of Bern, Bern, Switzerland
- Department of Otorhinolaryngology, Head and Neck Surgery, Kantonsspital Baselland, Liestal, Switzerland
| | - Urs Borner
- Department of Otorhinolaryngology, Head and Neck Surgery, Inselspital, University Hospital and University of Bern, Bern, Switzerland
| | - Marco Caversaccio
- Department of Otorhinolaryngology, Head and Neck Surgery, Inselspital, University Hospital and University of Bern, Bern, Switzerland
| | - Kurt Tschopp
- Department of Otorhinolaryngology, Head and Neck Surgery, Kantonsspital Baselland, Liestal, Switzerland
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Sacco G, Mercuri M, Hornung R, Visser H, Lorato I, Pisa S, Dolmans G. A SISO FMCW radar based on inherently frequency scanning antennas for 2-D indoor tracking of multiple subjects. Sci Rep 2023; 13:16701. [PMID: 37794080 PMCID: PMC10551012 DOI: 10.1038/s41598-023-41541-3] [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: 03/24/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
The contextual non-invasive monitoring and tracking of multiple human targets for health and surveillance purposes is an increasingly investigated application. Radars are good candidates, since they are able to remotely monitor people without raising privacy concerns. However, radar systems are typically based on complex architectures involving multiple channels and antennas, such as multiple-input and multiple-output (MIMO) or electronic beam scanning, resulting also in a high power consumption. In contrast with existing technologies, this paper proposes a single-input and single-output (SISO) frequency-modulated continuous wave (FMCW) radar in combination with frequency scanning antennas for tracking multiple subjects in indoor environments. A data processing method is also presented for angular separation and clutter removal. The system was successfully tested in five realistic indoor scenarios involving paired subjects, which were either static or moving along predefined paths varying their range and angular position. In all scenarios, the radar was able to track the targets, reporting a maximum mean absolute error (MAE) of 20 cm and 5.64[Formula: see text] in range and angle, respectively. Practical applications arise for ambient assisted living, telemedicine, smart building applications and surveillance.
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Affiliation(s)
- Giulia Sacco
- Institut d'Électronique et des Technologies du numéRique (IETR), University of Rennes, UMR CNRS 6164, 35000, Rennes, France.
| | - Marco Mercuri
- Dipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036, Rende, CS, Italy
| | | | - Huib Visser
- imec-Netherlands, 5656 AE, Eindhoven, The Netherlands
| | - Ilde Lorato
- imec-Netherlands, 5656 AE, Eindhoven, The Netherlands
| | - Stefano Pisa
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184, Rome, Italy
| | - Guido Dolmans
- imec-Netherlands, 5656 AE, Eindhoven, The Netherlands
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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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Zhang X, Yang C, Xiao Z, Lu B, Zhang J, Li J, Liu C. A novel target state detection method for accurate cardiopulmonary signal extraction based on FMCW radar signals. Front Physiol 2023; 14:1206471. [PMID: 37435306 PMCID: PMC10330764 DOI: 10.3389/fphys.2023.1206471] [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: 04/15/2023] [Accepted: 06/08/2023] [Indexed: 07/13/2023] Open
Abstract
Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select the range bin containing the target cardiopulmonary signal. In this paper, we propose a target range bin selection algorithm based on a mixed-modal information threshold. We introduce a confidence value in the frequency domain to determine the state of the human target and employ the range bin variance in the time domain to determine the range bin change status of the target. The proposed method accurately detects the state of the target and effectively selects the range bin containing the cardiopulmonary signal with a high signal-to-noise ratio. Experimental results demonstrate that the proposed method achieves better accuracy in cardiopulmonary signal rate estimation. Moreover, the proposed algorithm is lightweight in data processing and has good real-time performance.
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Affiliation(s)
- Xiaozheng Zhang
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Chenxi Yang
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | | | - Binbin Lu
- Chuhang Technology Co. Ltd., Nanjing, China
| | - Ji Zhang
- Chuhang Technology Co. Ltd., Nanjing, China
| | - Jianqing Li
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Chengyu Liu
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
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Mingle S, Kampouridou D, Feresidis A. Multi-Layer Beam Scanning Leaky Wave Antenna for Remote Vital Signs Detection at 60 GHz. SENSORS (BASEL, SWITZERLAND) 2023; 23:4059. [PMID: 37112399 PMCID: PMC10146583 DOI: 10.3390/s23084059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/21/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
A multi-layer beam-scanning leaky wave antenna (LWA) for remote vital sign monitoring (RVSM) at 60 GHz using a single-tone continuous-wave (CW) Doppler radar has been developed in a typical dynamic environment. The antenna's components are: a partially reflecting surface (PRS), high-impedance surfaces (HISs), and a plain dielectric slab. A dipole antenna works as a source together with these elements to produce a gain of 24 dBi, a frequency beam scanning range of 30°, and precise remote vital sign monitoring (RVSM) up to 4 m across the operating frequency range (58-66 GHz). The antenna requirements for the DR are summarised in a typical dynamic scenario where a patient is to have continuous monitoring remotely, while sleeping. During the continuous health monitoring process, the patient has the freedom to move up to one meter away from the fixed sensor position.The proposed multi-layer LWA system was placed at a distance of 2 m and 4 m from the test subject to confirm the suitability of the developed antenna for dynamic RVSM applications. A proper setting of the operating frequency range (58 to 66 GHz) enabled the detection of both heart beats and respiration rates of the subject within a 30° angular range.
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Bujan B, Fischer T, Dietz-Terjung S, Bauerfeind A, Jedrysiak P, Große Sundrup M, Hamann J, Schöbel C. Clinical validation of a contactless respiration rate monitor. Sci Rep 2023; 13:3480. [PMID: 36859403 PMCID: PMC9975830 DOI: 10.1038/s41598-023-30171-4] [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: 06/03/2022] [Accepted: 02/16/2023] [Indexed: 03/03/2023] Open
Abstract
Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.
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Affiliation(s)
- Bartosz Bujan
- Klinik Lengg AG, Neurorehabilitation Center, Bleulerstrasse 60, 8008, Zurich, Switzerland.
| | - Tobit Fischer
- grid.477805.90000 0004 7470 9004Essen University Hospital, Ruhrlandklinik, Tueschener Weg 40, 45239 Essen, Germany
| | - Sarah Dietz-Terjung
- grid.477805.90000 0004 7470 9004Essen University Hospital, Ruhrlandklinik, Tueschener Weg 40, 45239 Essen, Germany
| | - Aribert Bauerfeind
- grid.419749.60000 0001 2235 3868Klinik Lengg AG, Swiss Epilepsy Center, Bleulerstrasse 60, 8008 Zurich, Switzerland
| | - Piotr Jedrysiak
- Essen University Hospital, Neurorehabilitation Center, Bleulerstrasse 60, 8008 Zurich, Switzerland
| | - Martina Große Sundrup
- grid.477805.90000 0004 7470 9004Essen University Hospital, Ruhrlandklinik, Tueschener Weg 40, 45239 Essen, Germany
| | - Janne Hamann
- grid.419749.60000 0001 2235 3868Klinik Lengg AG, Swiss Epilepsy Center, Bleulerstrasse 60, 8008 Zurich, Switzerland
| | - Christoph Schöbel
- grid.477805.90000 0004 7470 9004Essen University Hospital, Ruhrlandklinik, Tueschener Weg 40, 45239 Essen, Germany
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Virtual Reality in Health Science Education: Professors’ Perceptions. MULTIMODAL TECHNOLOGIES AND INTERACTION 2022. [DOI: 10.3390/mti6120110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. Recent advances in technology position VR as a multipurpose technology in the healthcare sector and as a critical component in achieving Health 4.0. In this article, descriptive and correlationally quantitative research is carried out on the assessments made by Latin American health sciences university professors on the didactic use of virtual reality technologies. The main objective was to analyze the differences in the perceptions expressed by the public or private tenure of the universities where the professors teach. In addition, gender and age gaps were identified in the assessments obtained from each of the types of universities. The results reveal that Latin American health science professors at private universities have a higher selfconcept of their digital skills for the use of virtual reality in the lectures. This greater selfconcept also leads to a reduction in the gender and age gaps in the participating private universities with respect to the public counterparts. It is advisable to increase both faculty training in the didactic use of virtual reality and funding for its use, mainly in public universities.
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Pini N, Ong JL, Yilmaz G, Chee NIYN, Siting Z, Awasthi A, Biju S, Kishan K, Patanaik A, Fifer WP, Lucchini M. An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device. Front Neurosci 2022; 16:974192. [PMID: 36278001 PMCID: PMC9584568 DOI: 10.3389/fnins.2022.974192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Background The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG). Materials and methods This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch. Results We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusion The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.
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Affiliation(s)
- Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas I. Y. N. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhao Siting
- Electronic and Information Engineering, Imperial College London, London, United Kingdom
| | - Animesh Awasthi
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | - Siddharth Biju
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | | | | | - William P. Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
| | - Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
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Xiang M, Ren W, Li W, Xue Z, Jiang X. High-Precision Vital Signs Monitoring Method Using a FMCW Millimeter-Wave Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:7543. [PMID: 36236641 PMCID: PMC9572116 DOI: 10.3390/s22197543] [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: 08/26/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
The method of using millimeter-wave radar sensors to detect human vital signs, namely respiration and heart rate, has received widespread attention in non-contact monitoring. These sensors are compact, lightweight, and able to sense and detect various scenarios. However, it still faces serious problems of noisy interference in hardware, which leads to a low signal-to-noise ratio (SNR). We used a frequency-modulated continuous wave (FMCW) radar sensor operating at 77 GHz in an office environment to extract the respiration and heart rate of a person accustomed to sitting in a chair. Indeed, the proposed signal processing includes novel impulse denoising operations and the spectral estimation decision method, which are unique in terms of noise reduction and accuracy improvement. In addition, the proposed method provides high-quality, repeatable respiration and heart rates with relative errors of 1.33% and 1.96% on average compared with the reference values measured by a reliable smart bracelet.
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Affiliation(s)
- Mingxu Xiang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Wu Ren
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Weiming Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Zhenghui Xue
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Xinyue Jiang
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Purnomo AT, Komariah KS, Lin DB, Hendria WF, Sin BK, Ahmadi N. Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:664-678. [PMID: 35853073 PMCID: PMC9647724 DOI: 10.1109/tbcas.2022.3192359] [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: 01/21/2022] [Revised: 03/30/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.
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Affiliation(s)
- Ariana Tulus Purnomo
- Department of Electronic and Computer EngineeringNational Taiwan University of Science and TechnologyTaipei10607Taiwan
| | - Kokoy Siti Komariah
- Department of AI Convergence and the Division of Computer Engineering (respectively)Pukyong National UniversityBusan48513Republic of Korea
| | - Ding-Bing Lin
- Department of Electronic and Computer EngineeringNational Taiwan University of Science and TechnologyTaipei10607Taiwan
| | - Willy Fitra Hendria
- Department of Intelligent Mechatronics EngineeringSejong UniversitySeoul05006Republic of Korea
| | - Bong-Kee Sin
- Department of AI Convergence and the Division of Computer Engineering (respectively)Pukyong National UniversityBusan48513Republic of Korea
| | - Nur Ahmadi
- Center for Artificial Intelligence (U-CoE AI-VLB), School of Electrical Engineering and InformaticsBandung Institute of TechnologyBandung40132Indonesia
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15
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Hernandez-Aguila M, Olvera-Cervantes JL, Perez-Ramos AE, Corona-Chavez A. Methodology for the determination of human respiration rate by using Doppler radar and Empirical Modal Decomposition. Sci Rep 2022; 12:8675. [PMID: 35606407 PMCID: PMC9127072 DOI: 10.1038/s41598-022-12726-z] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 05/09/2022] [Indexed: 12/02/2022] Open
Abstract
In this work, a methodology is presented for the determination of the respiration rate of a person under test (PUT), the detection of movements, as well as the elimination of the spurious effects produced by the movements of the PUT. The methodology is based on Empirical Modal Decomposition (EMD) applied to the phase signal obtained by means of a quadrature Doppler radar operating in S band. The EMD allows to automatically eliminate the continuos component (CC) which is present in the phase signal since one of the main characteristics of the modes generated by the EMD is that its mean is equal to zero. On the other hand, the first mode of the EMD is used for the detection of movements while the sum of the second and third modes are used for the elimination of the CC drift caused by the DC drift and the high frequency components produced by the movements of the PUT. The proposed methodology was successfully tested in a PUT at rest and performing movements of the head, arm and combination of head, arm, and torso. The average respiration rate measured was 20.78 breaths / min with a standard deviation of 2.53 breaths/min.
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Affiliation(s)
| | | | - Aldo-Eleazar Perez-Ramos
- CONACyT-CICESE-Monterrey, Apodaca, Mexico
- Department of Electronics Engineering, TecNM Campus Oaxaca - ITO, Oaxaca,, Mexico
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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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Ishmael K, Zheng Y, Borić-Lubecke O. Phase Correlation Single Channel Continuous Wave Doppler Radar Recognition of Multiple Sources. SENSORS 2022; 22:s22030970. [PMID: 35161717 PMCID: PMC8840519 DOI: 10.3390/s22030970] [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: 12/22/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 02/05/2023]
Abstract
Continuous-wave Doppler radar (CWDR) can be used to remotely detect physiological parameters, such as respiration and heart signals. However, detecting and separating multiple targets remains a challenging task for CWDR. While complex transceiver architectures and advanced signal processing algorithms have been demonstrated as effective for multiple target separations in some scenarios, the separation of equidistant sources within a single antenna beam remains a challenge. This paper presents an alternative phase tuning approach that exploits the diversity among target distances and physiological parameters for multi-target detection. The design utilizes a voltage-controlled analog phase shifter to manipulate the phase correlation of the CWDR and thus create different signal mixtures from the multiple targets, then separates them in the frequency domain by suppressing individual signals sequentially. We implemented the phase correlation system based on a 2.4 GHz single-channel CWDR and evaluated it against multiple mechanical and human targets. The experimental results demonstrated successful separation of nearly equidistant targets within an antenna beam, equivalent to separating physiological signals of two people seated shoulder to shoulder.
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Lauteslager T, Maslik M, Siddiqui F, Marfani S, Leschziner GD, Williams AJ. Validation of a New Contactless and Continuous Respiratory Rate Monitoring Device Based on Ultra-Wideband Radar Technology. SENSORS 2021; 21:s21124027. [PMID: 34207961 PMCID: PMC8230718 DOI: 10.3390/s21124027] [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: 05/13/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 11/16/2022]
Abstract
Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland-Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were -3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, -2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and -1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.
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Iwata Y, Thanh HT, Sun G, Ishibashi K. High Accuracy Heartbeat Detection from CW-Doppler Radar Using Singular Value Decomposition and Matched Filter. SENSORS 2021; 21:s21113588. [PMID: 34064145 PMCID: PMC8196719 DOI: 10.3390/s21113588] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/04/2021] [Accepted: 05/18/2021] [Indexed: 11/22/2022]
Abstract
Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as 1.93±1.76bpm and 57.0±28.1s for the lying posture, and 9.72±7.86bpm and 81.3±24.3s for the sitting posture.
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Affiliation(s)
- Yuki Iwata
- Graduate School of Informatics and Engineering, The University of Electro-Communications (UEC), Tokyo 182-8585, Japan; (G.S.); (K.I.)
- Correspondence:
| | - Han Trong Thanh
- School of Electronics and Telecommunications, Hanoi University of Science and Technology (HUST), Hanoi 100000, Vietnam;
| | - Guanghao Sun
- Graduate School of Informatics and Engineering, The University of Electro-Communications (UEC), Tokyo 182-8585, Japan; (G.S.); (K.I.)
| | - Koichiro Ishibashi
- Graduate School of Informatics and Engineering, The University of Electro-Communications (UEC), Tokyo 182-8585, Japan; (G.S.); (K.I.)
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Wiegandt FC, Biegger D, Fast JF, Matusiak G, Mazela J, Ortmaier T, Doll T, Dietzel A, Bohnhorst B, Pohlmann G. Detection of Breathing Movements of Preterm Neonates by Recording Their Abdominal Movements with a Time-of-Flight Camera. Pharmaceutics 2021; 13:pharmaceutics13050721. [PMID: 34068978 PMCID: PMC8156597 DOI: 10.3390/pharmaceutics13050721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/20/2022] Open
Abstract
In order to deliver an aerosolized drug in a breath-triggered manner, the initiation of the patient’s inspiration needs to be detected. The best-known systems monitoring breathing patterns are based on flow sensors. However, due to their large dead space volume, flow sensors are not advisable for monitoring the breathing of (preterm) neonates. Newly-developed respiratory sensors, especially when contact-based (invasive), can be tested on (preterm) neonates only with great effort due to clinical and ethical hurdles. Therefore, a physiological model is highly desirable to validate these sensors. For developing such a system, abdominal movement data of (preterm) neonates are required. We recorded time sequences of five preterm neonates’ abdominal movements with a time-of-flight camera and successfully extracted various breathing patterns and respiratory parameters. Several characteristic breathing patterns, such as forced breathing, sighing, apnea and crying, were identified from the movement data. Respiratory parameters, such as duration of inspiration and expiration, as well as respiratory rate and breathing movement over time, were also extracted. This work demonstrated that respiratory parameters of preterm neonates can be determined without contact. Therefore, such a system can be used for breathing detection to provide a trigger signal for breath-triggered drug release systems. Furthermore, based on the recorded data, a physiological abdominal movement model of preterm neonates can now be developed.
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Affiliation(s)
- Felix C. Wiegandt
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Correspondence: (F.C.W.); (G.P.); Tel.: +49-511-5350-287 (F.C.W.); +49-511-5350-116 (G.P.)
| | - David Biegger
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
| | - Jacob F. Fast
- Institute of Mechatronic Systems, Leibniz Universität Hannover, 30823 Garbsen, Germany; (J.F.F.); (T.O.)
- Department of Phoniatrics and Pediatric Audiology, Hannover Medical School, 30625 Hannover, Germany
| | - Grzegorz Matusiak
- Division of Infectious Diseases, Department of Neonatology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (G.M.); (J.M.)
| | - Jan Mazela
- Division of Infectious Diseases, Department of Neonatology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (G.M.); (J.M.)
| | - Tobias Ortmaier
- Institute of Mechatronic Systems, Leibniz Universität Hannover, 30823 Garbsen, Germany; (J.F.F.); (T.O.)
| | - Theodor Doll
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Department of Otorhinolaryngology, Hannover Medical School, 30625 Hannover, Germany
| | - Andreas Dietzel
- Institute of Microtechnology, Technische Universität Braunschweig, 38124 Braunschweig, Germany;
| | - Bettina Bohnhorst
- Department of Pediatric Pulmonology, Allergology and Neonatology, Hannover Medical School, 30625 Hannover, Germany;
| | - Gerhard Pohlmann
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Correspondence: (F.C.W.); (G.P.); Tel.: +49-511-5350-287 (F.C.W.); +49-511-5350-116 (G.P.)
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Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band. SENSORS 2021; 21:s21082732. [PMID: 33924439 PMCID: PMC8070581 DOI: 10.3390/s21082732] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 03/29/2021] [Accepted: 04/10/2021] [Indexed: 11/17/2022]
Abstract
A non-contact heartbeat/respiratory rate monitoring system was designed using narrow beam millimeter wave radar. Equipped with a special low sidelobe and small-sized antenna lens at the front end of the receiving and transmitting antennas in the 120 GHz band of frequency-modulated continuous-wave (FMCW) system, this sensor system realizes the narrow beam control of radar, reduces the interference caused by the reflection of other objects in the measurement background, improves the signal-to-clutter ratio (SCR) of the intermediate frequency signal (IF), and reduces the complexity of the subsequent signal processing. In order to solve the problem that the accuracy of heart rate is easy to be interfered with by respiratory harmonics, an adaptive notch filter was applied to filter respiratory harmonics. Meanwhile, the heart rate obtained by fast Fourier transform (FFT) was modified by using the ratio of adjacent elements, which helped to improve the accuracy of heart rate detection. The experimental results show that when the monitoring system is 1 m away from the human body, the probability of respiratory rate detection error within ±2 times for eight volunteers can reach 90.48%, and the detection accuracy of the heart rate can reach 90.54%. Finally, short-term heart rate measurement was realized by means of improved empirical mode decomposition and fast independent component analysis algorithm.
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Rodrigues DVQ, Li C. A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2021; 21:2612. [PMID: 33917801 PMCID: PMC8068160 DOI: 10.3390/s21082612] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/27/2021] [Accepted: 04/06/2021] [Indexed: 11/16/2022]
Abstract
Portable, low-cost, microwave radars have attracted researchers' attention for being an alternative noncontact solution for structural condition monitoring. In addition, by leveraging their capability of providing the target velocity information, the radar-based remote monitoring of complex rotating structures can also be accomplished. Modern radar systems are compact, able to be easily integrated in sensor networks, and can deliver high accuracy measurements. This paper reviews the recent technical advances in low-cost Doppler radar systems for phase-demodulated displacement measurements and time-Doppler analysis for structural health information, including digital signal processing and emerging applications related to radar sensor networks.
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Affiliation(s)
- Davi V. Q. Rodrigues
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;
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Short time cardio-vascular pulses estimation for dengue fever screening via continuous-wave Doppler radar using empirical mode decomposition and continuous wavelet transform. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102361] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Experimental Comparison of IR-UWB Radar and FMCW Radar for Vital Signs. SENSORS 2020; 20:s20226695. [PMID: 33238557 PMCID: PMC7768379 DOI: 10.3390/s20226695] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/11/2020] [Accepted: 11/14/2020] [Indexed: 11/17/2022]
Abstract
In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel.
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25
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Tsai YC, Lai SH, Ho CJ, Wu FM, Henrickson L, Wei CC, Chen I, Wu V, Chen J. High Accuracy Respiration and Heart Rate Detection Based on Artificial Neural Network Regression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:232-235. [PMID: 33017971 DOI: 10.1109/embc44109.2020.9175161] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A 24GHz Doppler radar system for accurate contactless monitoring of heart and respiratory rates is demonstrated here. High accuracy predictions are achieved by employing a CNN+LSTM neural network architecture for regression analysis. Detection accuracies of 99% and 98% have been attained for heart rate and respiration rate, respectively.Clinical Relevance- This work establishes a non-contact radar system with 99% detection accuracy for a heart rate variability warning system. This system can enable convenient and fast monitoring for daily care at home.
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26
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Kim H, Jeong J. Non-Contact Measurement of Human Respiration and Heartbeat Using W-band Doppler Radar Sensor. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5209. [PMID: 32932671 PMCID: PMC7570872 DOI: 10.3390/s20185209] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 11/25/2022]
Abstract
This paper presents a W-band continuous-wave (CW) Doppler radar sensor for non-contact measurement of human respiration and heartbeat. The very short wavelength of the W-band signal allows a high-precision detection of the displacement of the chest surface by the heartbeat as well as respiration. The CW signal at 94 GHz is transmitted through a high-gain horn antenna to the human chest at a distance of 1 m. The phase-modulated reflection signal is down-converted to the baseband by the quadrature mixer with an excellent amplitude and phase matches between I and Q channels, which makes the IQ mismatch correction in the digital domain unnecessary. The baseband I and Q data are digitized using data acquisition (DAQ) board. The arctangent demodulation with automatic phase unwrapping is applied to the low-pass filtered I and Q data to effectively solve the null point problem. A slow-varying DC component is rejected in the demodulated signal by the trend removal algorithm. Then, the respiration signal with a frequency of 0.27 Hz and a displacement of ~6.1 mm is retrieved by applying a low-pass filter. Finally, the respiration signal is removed by the band-pass filter and the heartbeat signal is extracted, showing a frequency of 1.35 Hz and a displacement of ~0.26 mm. The extracted respiration and heartbeat rates are very close to the manual measurement results. The demonstrated W-band CW radar sensors can be easily applied to find the angular location of the human body by using a phased array under a compact size.
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Affiliation(s)
| | - Jinho Jeong
- Department of Electronic Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea;
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27
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Sacco G, Piuzzi E, Pittella E, Pisa S. An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3489. [PMID: 32575677 PMCID: PMC7348911 DOI: 10.3390/s20123489] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022]
Abstract
This work tests the ability of fmcw radar to measure the respiratory rate and the heartbeat of a subject in challenging indoor scenarios. To simulate a realistic configuration for ambient assisted living (AAL) applications, in which the thorax orientation towards the antenna is typically unknown, four different scenarios were considered. Measurements were performed on five volunteers positioned with the chest, left, back, and right side facing the antenna, respectively. The 5 . 8 radar and the antennas used for the measurements were suitably designed for the considered application. To obtain a low cost and compact system, series-fed arrays were preferred over other antenna topologies. The geometry of the patches was opportunely shaped to reduce the side lobe level (SLL) and increase the bandwidth, thus ensuring good system performances. In all scenarios, the vital signs extracted from the radar signal were compared with the ones collected by a photoplethysmograph and a respiratory belt, used as references. A statistical analysis of the measured data on the different subjects and orientations was performed, showing that the radar was able to measure with high accuracy both the respiratory rate and the heartbeat in all considered configurations.
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Affiliation(s)
- Giulia Sacco
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy; (E.P.); (S.P.)
| | - Emanuele Piuzzi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy; (E.P.); (S.P.)
| | - Erika Pittella
- Department of Legal and Economic Sciences, Pegaso University, 00186 Rome, Italy;
| | - Stefano Pisa
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy; (E.P.); (S.P.)
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28
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Malešević N, Petrović V, Belić M, Antfolk C, Mihajlović V, Janković M. Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2351. [PMID: 32326190 PMCID: PMC7219229 DOI: 10.3390/s20082351] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/16/2020] [Accepted: 04/19/2020] [Indexed: 11/22/2022]
Abstract
The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference ("ground truth") in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.
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Affiliation(s)
- Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Box 118, 221 00 Lund, Sweden;
| | - Vladimir Petrović
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
| | - Minja Belić
- Novelic, Veljka Dugoševića 54/A3, 11000 Belgrade, Serbia; (M.B.); (V.M.)
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Box 118, 221 00 Lund, Sweden;
| | - Veljko Mihajlović
- Novelic, Veljka Dugoševića 54/A3, 11000 Belgrade, Serbia; (M.B.); (V.M.)
| | - Milica Janković
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
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29
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Arumugam S, Colburn DAM, Sia SK. Biosensors for Personal Mobile Health: A System Architecture Perspective. ADVANCED MATERIALS TECHNOLOGIES 2020; 5:1900720. [PMID: 33043127 PMCID: PMC7546526 DOI: 10.1002/admt.201900720] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Indexed: 05/29/2023]
Abstract
Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.
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Affiliation(s)
- Siddarth Arumugam
- Department of Biomedical Engineering, Columbia University, 10027 New York, United States
| | - David A M Colburn
- Department of Biomedical Engineering, Columbia University, 10027 New York, United States
| | - Samuel K Sia
- Department of Biomedical Engineering, Columbia University, 10027 New York, United States
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30
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Benammar M, Alassi A, Gastli A, Ben-Brahim L, Touati F. New Fast Arctangent Approximation Algorithm for Generic Real-Time Embedded Applications. SENSORS 2019; 19:s19235148. [PMID: 31775303 PMCID: PMC6928950 DOI: 10.3390/s19235148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/15/2019] [Accepted: 11/19/2019] [Indexed: 11/29/2022]
Abstract
Fast and accurate arctangent approximations are used in several contemporary applications, including embedded systems, signal processing, radar, and power systems. Three main approximation techniques are well-established in the literature, varying in their accuracy and resource utilization levels. Those are the iterative coordinate rotational digital computer (CORDIC), the lookup tables (LUTs)-based, and the rational formulae techniques. This paper presents a novel technique that combines the advantages of both rational formulae and LUT approximation methods. The new algorithm exploits the pseudo-linear region around the tangent function zero point to estimate a reduced input arctangent through a modified rational approximation before referring this estimate to its original value using miniature LUTs. A new 2nd order rational approximation formula is introduced for the first time in this work and benchmarked against existing alternatives as it improves the new algorithm performance. The eZDSP-F28335 platform has been used for practical implementation and results validation of the proposed technique. The contributions of this work are summarized as follows: (1) introducing a new approximation algorithm with high precision and application-based flexibility; (2) introducing a new rational approximation formula that outperforms literature alternatives with the algorithm at higher accuracy requirement; and (3) presenting a practical evaluation index for rational approximations in the literature.
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Affiliation(s)
- Mohieddine Benammar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (A.G.); (L.B.-B.); (F.T.)
| | - Abdulrahman Alassi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (A.G.); (L.B.-B.); (F.T.)
- R&D Laboratory, Iberdrola Innovation Middle East, Doha 210177, Qatar
- Correspondence:
| | - Adel Gastli
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (A.G.); (L.B.-B.); (F.T.)
| | - Lazhar Ben-Brahim
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (A.G.); (L.B.-B.); (F.T.)
| | - Farid Touati
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (A.G.); (L.B.-B.); (F.T.)
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31
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Peng Z, Li C. Portable Microwave Radar Systems for Short-Range Localization and Life Tracking: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1136. [PMID: 30845720 PMCID: PMC6427700 DOI: 10.3390/s19051136] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/01/2019] [Accepted: 03/01/2019] [Indexed: 12/02/2022]
Abstract
Short-range localization and life tracking have been hot research topics in the fields of medical care, consumer electronics, driving assistance, and indoor robots/drones navigation. Among various sensors, microwave and mm-wave continuous-wave (CW) radar sensors are gaining more popularity in their intrinsic advantages such as simple architecture, easy system integration, high accuracy, relatively low cost, and penetration capability. This paper reviews the recent advances in CW radar systems for short-range localization and life tracking applications, including system improvement, signal processing, as well as the emerging applications integrated with machine learning.
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Affiliation(s)
| | - Changzhi Li
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
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32
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Arab H, Dufour S, Moldovan E, Akyel C, Tatu SO. A 77-GHz Six-Port Sensor for Accurate Near-Field Displacement and Doppler Measurements. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2565. [PMID: 30082587 PMCID: PMC6111671 DOI: 10.3390/s18082565] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 07/03/2018] [Accepted: 08/01/2018] [Indexed: 11/17/2022]
Abstract
A continuous-wave (CW) radar sensor design based on a millimetre-wave six-port interferometer is proposed. A complete sensor prototype is conceived of, fabricated and measured at 77 GHz for short-range professional and industrial applications. This sensor is designed to measure distances and Doppler frequencies with high accuracy, at a reasonable cost. Accurate phase measurements are also performed using the six-port technology, which makes it a promising candidate for CW radar sensing applications. Advances in the performance and functionality of six-port sensors are surveyed to highlight recent progress in this area. These include improvements in design, low power consumption, high signal to noise ratio, compactness, robustness and simplicity in realization. Given the fact that they are easy to fabricate, due to the lack of active circuits and being highly accurate, it is expected that six-port sensors will significantly contribute to the development of human tracking devices and industrial sensors in the near future. The entire circuit prototype, including the transmitter, the receiver antenna, the six-port interferometer and the four power detectors have been integrated on a die. The circuit is fabricated using a hybrid integrated technology on a 127-μm ceramic substrate with a relative permittivity of εr=9.8. Calibrated tuning forks are used to assess the performance of the six-port sensor experimentally for various frequencies.
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Affiliation(s)
- Homa Arab
- Institut National de la Recherche Scientifique-Centre Énergie Matériaux Télécommunications, Montréal, QC H5A 1K6, Canada.
| | - Steven Dufour
- École Polytechnique de Montréal, Montréal, QC H3C 3A7, Canada.
| | - Emilia Moldovan
- Institut National de la Recherche Scientifique-Centre Énergie Matériaux Télécommunications, Montréal, QC H5A 1K6, Canada.
| | - Cevdet Akyel
- École Polytechnique de Montréal, Montréal, QC H3C 3A7, Canada.
| | - Serioja O Tatu
- Institut National de la Recherche Scientifique-Centre Énergie Matériaux Télécommunications, Montréal, QC H5A 1K6, Canada.
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33
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Alimenti F, Palazzi V, Mariotti C, Virili M, Orecchini G, Bonafoni S, Roselli L, Mezzanotte P. A 24-GHz Front-End Integrated on a Multilayer Cellulose-Based Substrate for Doppler Radar Sensors. SENSORS 2017; 17:s17092090. [PMID: 28895914 PMCID: PMC5621086 DOI: 10.3390/s17092090] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 08/29/2017] [Accepted: 09/07/2017] [Indexed: 11/16/2022]
Abstract
This paper presents a miniaturized Doppler radar that can be used as a motion sensor for low-cost Internet of things (IoT) applications. For the first time, a radar front-end and its antenna are integrated on a multilayer cellulose-based substrate, built-up by alternating paper, glue and metal layers. The circuit exploits a distributed microstrip structure that is realized using a copper adhesive laminate, so as to obtain a low-loss conductor. The radar operates at 24 GHz and transmits 5 mW of power. The antenna has a gain of 7.4 dBi and features a half power beam-width of 48 degrees. The sensor, that is just the size of a stamp, is able to detect the movement of a walking person up to 10 m in distance, while a minimum speed of 50 mm/s up to 3 m is clearly measured. Beyond this specific result, the present paper demonstrates that the attractive features of cellulose, including ultra-low cost and eco-friendliness (i.e., recyclability and biodegradability), can even be exploited for the realization of future high-frequency hardware. This opens opens the door to the implementation on cellulose of devices and systems which make up the "sensing layer" at the base of the IoT ecosystem.
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Affiliation(s)
- Federico Alimenti
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
| | - Valentina Palazzi
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
| | - Chiara Mariotti
- Infineon Technologies Austria AG, Siemensstrasse 2, 9500 Villach, Austria.
| | - Marco Virili
- Qorvo Munich GmbH, Konrad-Zuse-Platz 1, 81829 Munich, Germany.
| | - Giulia Orecchini
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
| | - Stefania Bonafoni
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
| | - Luca Roselli
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
| | - Paolo Mezzanotte
- Department of Engineering, University of Perugia, 06125 Perugia, Italy.
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34
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Liu L, Guo C, Li J, Xu H, Zhang J, Wang B. Simultaneous Life Detection and Localization Using a Wideband Chaotic Signal with an Embedded Tone. SENSORS 2016; 16:s16111866. [PMID: 27827976 PMCID: PMC5134525 DOI: 10.3390/s16111866] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 11/02/2016] [Indexed: 11/16/2022]
Abstract
A hybrid life detection radar system which transmits a wideband chaotic signal containing an embedded single-tone is proposed. The chaotic signal is used for target localization by the time-domain correlation method and synthetic aperture technique, and the single-tone signal is used to measure the frequencies of breathing and heartbeat based on an on-chip split-ring integrated sensor and Michelson interference principle. Experimental results in free space and in through-wall scenarios demonstrate that the system can realize human detection and localization simultaneously with high range resolution, high sensitivity, and large dynamic range without complex signal processing. The range resolution is about 10 cm, and the dynamic range is 35 dB for the respiration signal detection and 25 dB for the heartbeat signal detection. Due to its good immunity to interference/jamming and high spectrum efficiency, the proposed system is suitable for post-disaster rescue, elder/infant/patient vitality monitoring, and anti-terrorism enforcement applications.
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Affiliation(s)
- Li Liu
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Chaoyi Guo
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Jingxia Li
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Hang Xu
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Jianguo Zhang
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Bingjie Wang
- Key Laboratory of Advanced Transducers & Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
- College of Physics & Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
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35
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Wrist Pulse Rate Monitor Using Self-Injection-Locked Radar Technology. BIOSENSORS-BASEL 2016; 6:bios6040054. [PMID: 27792176 PMCID: PMC5192374 DOI: 10.3390/bios6040054] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/07/2016] [Accepted: 10/19/2016] [Indexed: 11/29/2022]
Abstract
To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected the respiratory rates and heart rates of a subject at a one-meter distance using a self-injection-locked (SIL) radar and a conventional continuous-wave (CW) radar to compare the sensitivity versus power consumption between the two radars. Then, a pulse rate monitor was constructed based on a bistatic SIL radar architecture. This monitor uses an active antenna that is composed of a SIL oscillator (SILO) and a patch antenna. When attached to a band worn on the subject’s wrist, the active antenna can monitor the pulse on the subject’s wrist by modulating the SILO with the associated Doppler signal. Subsequently, the SILO’s output signal is received and demodulated by a remote frequency discriminator to obtain the pulse rate information.
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