Stepped-Frequency Continuous-Wave Radar With Self-Injection-Locking Technology for Monitoring Multiple Human Vital Signs

Su, Wei-Chih; Tang, Mu-Cyun; Arif, Rezki El; Horng, Tzyy‐Sheng; Wang, Fu-Kang

doi:10.1109/tmtt.2019.2933199

Cited by 66 publications

(36 citation statements)

References 28 publications

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“…Meanwhile, most article on the implementation of SFCW radars for human HR and BR detection use frequency domain processing, peak detection from FFT being the dominant method. The baseband data obtained from the radar is put into a time-frequency matrix of J × N size, where J and N indicate the total number of frequency sweeps and the number of radio frequency (RF) steps, respectively [145]. Next a consecutive FFT is applied to the matrix to obtain a range Doppler profile seen in Figure 15c.…”

Section: Algorithms and Signal Processingmentioning

confidence: 99%

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Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

137

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Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

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Section: Algorithms and Signal Processingmentioning

confidence: 99%

“…(a) Time-domain SFCW radar data with presence of human subject after applying inverse fast Fourier transform (IFFT); (b) vital signs after cancelling the clutter effect[146]; (c) algorithm used to obtain the range-Doppler profile[145].…”

mentioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

137

View full text Add to dashboard Cite

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“…This paper examines an approach for recognizing three categories of sleep posture while simultaneously measuring diagnostic cardiopulmonary patterns using an unobtrusive and non-contact Doppler radar system. Previous applications of cardiopulmonary radar in the biomedical field have included monitoring cardiopulmonary-related motion characteristics of a single [16][17] and multiple subjects [18][19][20]. Prior research also successfully demonstrated sleep monitoring [21] with CW [22][23][24][25], FMCW [26][27], and UWB [28][29].…”

Section: Introductionmentioning

confidence: 99%

Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

et al. 2021

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While Doppler radar can be used to measure cardiopulmonary vital signs during sleep, meaningful diagnostic assessments are often subject to knowledge of a subject's changing sleep posture. The torso Effective Radar Cross Section (ERCS) and displacement magnitude were studied for 20 human subjects in three imitated sleep posture categories using a dual-frequency Doppler radar system in an exploratory examination of the feasibility of using radar to recognize body orientation. Box plot statistical analyses were performed for comparative assessment of ratio variations in ERCS and respiration depth for three different imitated sleep postures. The observed statistical trends and correlations were applied to a physical model to develop posture decision algorithms with initial supine posture data used as a reference. A single-frequency algorithm tracked postures without error for 90% of the subjects using 2.4 GHz data, and 80% using 5.8 GHz data. As accuracy limitations were complementary, a dual-frequency algorithm was developed which recognized postures without error for 100% of the subjects.

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“…Humans are detected via their breathing patterns and separated from non-human targets. Su et al [ 40 ] developed a prototype system for RR and HR monitoring of multiple humans using a single-conversion stepped-frequency continuous-wave radar. It combines a stepped-frequency continuous-wave radar and a self-injection-locked radar to benefit from the range resolution and the Doppler sensitivity of the two radars.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

Slapničar

Wang

Luštrek

2021

Sensors

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Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.

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Stepped-Frequency Continuous-Wave Radar With Self-Injection-Locking Technology for Monitoring Multiple Human Vital Signs

Cited by 66 publications

References 28 publications

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

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