A concurrent dual-band radar sensor for vital sign tracking and short-range positioning

AbstractThis paper presents a concurrent dual-band radar system for noncontact tracking of vital signs (e.g., respiration and heartbeat), and indoor short-range localization. The proposed sensor, which has been achieved with our own customized concurrent dual-band subsystems, operates at 1.67 and 2.06 GHz synchronously. Based on the Doppler principle, tiny vital signs are obtained by analysis of spectrum of the signals received at each individual frequency band. Moreover, the location of a target is estimated based on the phase difference between these two closely spaced frequencies. The azimuth information is obtained by beam scanning. Combining the results of range and azimuth information allows the radar system to plot two-dimensional maps. As a result, the proposed radar is capable of monitoring human’s life activities and tracking the location of individuals continuously. System-level experiments were carried out to reveal the versatile capability of the life activity monitoring system.

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Reliability of a wearable wireless patch for continuous remote monitoring of vital signs in patients recovering from major surgery: a clinical validation study from the TRaCINg trial

BMJ Open ◽

10.1136/bmjopen-2019-031150 ◽

2019 ◽

Vol 9 (8) ◽

pp. e031150 ◽

Cited By ~ 7

Author(s):

Candice Downey ◽

Shu Ng ◽

David Jayne ◽

David Wong

Keyword(s):

Monitoring System ◽

Vital Sign ◽

Vital Signs ◽

University Hospital ◽

Secondary Outcome ◽

Major Surgery ◽

Limits Of Agreement ◽

Temperature Bias ◽

Vital Signs Monitoring ◽

Recorded Data

ObjectiveTo validate whether a wearable remote vital signs monitor could accurately measure heart rate (HR), respiratory rate (RR) and temperature in a postsurgical patient population at high risk of complications.DesignManually recorded vital signs data were paired with vital signs data derived from the remote monitor set in patients participating in the Trial of Remote versus Continuous INtermittent monitoring (TRaCINg) study: a trial of continuous remote vital signs monitoring.SettingSt James’s University Hospital, UK.Participants51 patients who had undergone major elective general surgery.InterventionsThe intervention was the SensiumVitals monitoring system. This is a wireless patch worn on the patient’s chest that measures HR, RR and temperature continuously. The reference standard was nurse-measured manually recorded vital signs.Primary and secondary outcome measuresThe primary outcomes were the 95% limits of agreement between manually recorded and wearable patch vital sign recordings of HR, RR and temperature. The secondary outcomes were the percentage completeness of vital sign patch data for each vital sign.Results1135 nurse observations were available for analysis. There was no clinically meaningful bias in HR (1.85 bpm), but precision was poor (95% limits of agreement −23.92 to 20.22 bpm). Agreement was poor for RR (bias 2.93 breaths per minute, 95% limits of agreement −8.19 to 14.05 breaths per minute) and temperature (bias 0.82°C, 95% limits of agreement −1.13°C to 2.78°C). Vital sign patch data completeness was 72.8% for temperature, 59.2% for HR and 34.1% for RR. Distributions of RR in manually recorded measurements were clinically implausible.ConclusionsThe continuous monitoring system did not reliably provide HR consistent with nurse measurements. The accuracy of RR and temperature was outside of acceptable limits. Limitations of the system could potentially be overcome through better signal processing. While acknowledging the time pressures placed on nursing staff, inaccuracies in the manually recorded data present an opportunity to increase awareness about the importance of manual observations, particularly with regard to methods of manual HR and RR measurements.

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Verification and first test measurement of a microwave-based vital sign monitor

Advances in Radio Science ◽

10.5194/ars-17-249-2019 ◽

2019 ◽

Vol 17 ◽

pp. 249-256

Author(s):

Daniel Schmiech ◽

Aly Marnach ◽

Andreas R. Diewald

Keyword(s):

Monitoring System ◽

Premature Infants ◽

Vital Sign ◽

Region Of Interest ◽

Radar System ◽

Sensor System ◽

Breathing Rate ◽

Test Measurement ◽

Reflected Signal

Abstract. The authors present a microwave-based vital sign monitoring system to detect the breathing rate of premature infants in a baby incubator. The sensor is a four channel I/Q-radar system with adapted antennas to cover the predefined region of interest on the patient surface of the incubator. In order to prove the correct illumination of the reclining area an electromotive actuator and a pneumatic dummy is used. With a periodic and repeatable breathing simulation the reflected signal in the sensor system is measured and evaluated. In the publication the radar system in the baby incubator, the electromotive actuator and the infant dummy are presented.

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Unconstrained Vital Sign Monitoring System Using an Aortic Pulse Wave Sensor

Scientific Reports ◽

10.1038/s41598-019-53808-9 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Naoki Hagiyama ◽

Harutoyo Hirano ◽

Akihisa Mito ◽

Zu Soh ◽

Etsunori Fujita ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Monitoring System ◽

Vital Sign ◽

Nervous Activity ◽

Vital Signs ◽

Stimulus Presentation ◽

Autonomic Nervous Activity ◽

Psychological States ◽

Autonomic Nervous

AbstractThis paper proposes a novel unconstrained monitoring system that measures heart and respiratory rates and evaluates autonomic nervous activity based on heart rate variability. The proposed system measures the aortic pulse waves (APWs) of a patient via an APW sensor that comprises a single microphone integrated into a mattress. Vital signs (i.e., heart rate, respiratory rate) and autonomic nervous activity were analyzed using the measured APWs. In an experiment with supine and seated participants, vital signs calculated by the proposed system were compared with vital signs measured with commercial devices, and we obtained the correlations of r > 0.8 for the heart rates, r > 0.7 for the respiratory rates, and r > 0.8 for the heart rate variability indices. These results indicate that the proposed system can produce accurate vital sign measurements. In addition, we performed the experiment of image stimulus presentation and explored the relationships between the self-reported psychological states evoked by the stimulus and the measured vital signs. The results indicated that vital signs reflect psychological states. In conclusion, the proposed system demonstrated its ability to monitor health conditions by actions as simple as sitting or lying on the APW sensor.

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Implementing Internet of Things in a Remote Patient Medical Monitoring System

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.132 ◽

2018 ◽

Vol 6 (8) ◽

pp. 159 ◽

Cited By ~ 1

Author(s):

Ifeoma V. Ngonadi

Keyword(s):

Internet Of Things ◽

Monitoring System ◽

Medical Records ◽

Research Work ◽

Healthcare Delivery ◽

Vital Signs ◽

Personal Digital Assistant ◽

Computer Applications ◽

Medical Monitoring ◽

Remote Patient

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Remote patient monitoring enables the monitoring of patients’ vital signs outside the conventional clinical settings which may increase access to care and decrease healthcare delivery costs. This paper focuses on implementing internet of things in a remote patient medical monitoring system. This was achieved by writing two computer applications in java in which one simulates a mobile phone called the Intelligent Personal Digital Assistant (IPDA) which uses a data structure that includes age, smoking habits and alcohol intake to simulate readings for blood pressure, pulse rate and mean arterial pressure continuously every twenty five which it sends to the server. The second java application protects the patients’ medical records as they travel through the networks by employing a symmetric key encryption algorithm which encrypts the patients’ medical records as they are generated and can only be decrypted in the server only by authorized personnel. The result of this research work is the implementation of internet of things in a remote patient medical monitoring system where patients’ vital signs are generated and transferred to the server continuously without human intervention.

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Patient Monitoring System using Computer Vision for Emotional Recognition and Vital Signs Detection

2020 IEEE Student Conference on Research and Development (SCOReD) ◽

10.1109/scored50371.2020.9250950 ◽

2020 ◽

Author(s):

Ahmad Anwar Zainuddin ◽

Sakthyvell Superamaniam ◽

Andrea Christella Andrew ◽

Ramanand Muraleedharan ◽

John Rakshys ◽

...

Keyword(s):

Computer Vision ◽

Monitoring System ◽

Patient Monitoring ◽

Vital Signs ◽

Emotional Recognition ◽

Patient Monitoring System

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Airborne Dual-Band Microwave Radar System for Snow Thickness Measurement

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323958 ◽

2020 ◽

Author(s):

Drew Taylor ◽

Stephen Yan ◽

Charles O'Neill ◽

Prasad Gogineni ◽

Sevgi Gurbuz ◽

...

Keyword(s):

Thickness Measurement ◽

Radar System ◽

Dual Band ◽

Microwave Radar ◽

Snow Thickness

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Recent Development of Non-Contact Multi-Target Vital Sign Detection and Location Tracking Based on Metamaterial Leaky Wave Antennas

Sensors ◽

10.3390/s21113619 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3619

Author(s):

Yichao Yuan ◽

Chung-Tse Michael Wu

Keyword(s):

Vital Sign ◽

Complex Structure ◽

Health Condition ◽

Phase Shifters ◽

Location Tracking ◽

Radar Sensor ◽

Leaky Wave ◽

Multiple Targets ◽

Microwave Radar ◽

Sign Detection

Microwave radar sensors have been developed for non-contact monitoring of the health condition and location of targets, which will cause minimal discomfort and eliminate sanitation issues, especially in a pandemic situation. To this end, several radar sensor architectures and algorithms have been proposed to detect multiple targets at different locations. Traditionally, beamforming techniques incorporating phase shifters or mechanical rotors are utilized, which is relatively complex and costly. On the other hand, metamaterial (MTM) leaky wave antennas (LWAs) have a unique property of launching waves of different spectral components in different directions. This feature can be utilized to detect multiple targets at different locations to obtain their healthcare and location information accurately, without complex structure and high cost. To this end, this paper reviews the recent development of MTM LWA-based radar sensor architectures for vital sign detection and location tracking. The experimental results demonstrate the effectiveness of MTM vital sign radar compared with different radar sensor architectures.

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Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽

10.3390/s21155228 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5228

Author(s):

Jin-Cheol Kim ◽

Hwi-Gu Jeong ◽

Seongwook Lee

Keyword(s):

Millimeter Wave ◽

Radar System ◽

Sensor Data ◽

Test Field ◽

Target Classification ◽

Radar Sensor ◽

Millimeter Wave Radar ◽

Direction Estimation ◽

Wave Radar ◽

Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

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