Multiple Subject Respiratory Pattern Recognition and Estimation of Direction of Arrival using Phase-Comparison Monopulse Radar

Islam, Shekh Md Mahmudul; Yavari, Ehsan; Rahman, Ashikur; Lubecke, Victor M.; Borić–Lubecke, Olga

doi:10.1109/rws.2019.8714272

Cited by 24 publications

(23 citation statements)

References 5 publications

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“…The characterization of chest wall motion has been addressed in many methodological works, which presented interesting results, including for the general (e.g., motion quantification) and specific levels (e.g., illness detection/severity assessment and scoring). This can be grouped as follows: [ 57 , 61 , 62 , 64 , 68 , 72 , 117 , 120 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 ] (2010–2015), [ 3 , 10 , 47 , 48 , 49 , 53 , 54 , 95 , 97 , 99 , 101 , 105 , 111 , 122 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 ...…”

Section: Resultsmentioning

confidence: 99%

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Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

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Section: Resultsmentioning

confidence: 99%

“…In the last few years, a variety of radar-based sensors has been investigated for respiration monitoring [ 44 , 51 , 69 , 82 , 94 , 95 , 96 , 97 ]. In 2019, Nosrati et al [ 94 ] proposed a phased array approach with a multiple-input multiple-output (MIMO) beamforming design.…”

Section: Resultsmentioning

confidence: 99%

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma

Noumeir

Essouri

et al. 2020

Sensors

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show abstract

“…Recent works tried to overcome such a limit. An SNR-based intelligent decision algorithm integrated two different approaches to isolate respiratory signatures of two subjects within the radar beamwidth [115]: Independent Component Analysis with the JADE algorithm (ICA-JADE) [116] and DOA [117], as shown in Fig. 8.…”

Section: B Crowd Detection and Signal-of-interest Extractionmentioning

confidence: 99%

Sensing of Life Activities at the Human-Microwave Frontier

Lubecke

Borić–Lubecke

et al. 2021

IEEE J. Microw.

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“…Prior research has demonstrated the feasibility of utilizing Doppler radar for tracking breathing rate and heart rate accurately (Lin, 1975, Islam et al, 2019aDroitcour et al, 2001;Droitcour, 2006;Li et al, 2013). Moreover, the feasibility of extracting different abnormal respiratory patterns using microwave Doppler radar has been investigated (Islam et al, 2019b;Lee et al, 2014). Figure 4 illustrates the spectral density (FFT) and continuous wavelet transform of radar captured normal (Figure 4A) and Cheyne-Stokes (Figure 4B) breathing patterns.…”

Section: Remote Breathing Monitoringmentioning

confidence: 99%

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

2021

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COVID-19, caused by SARS-CoV-2, is now a global pandemic disease. This outbreak has affected every aspect of life including work, leisure, and interaction with technology. Governments around the world have issued orders for travel bans, social distancing, and lockdown to control the spread of the virus and prevent strain on hospitals. This paper explores potential applications for radar-based non-contact remote respiration sensing technology that may help to combat the COVID-19 pandemic, and outlines potential advantages that may also help to reduce the spread of the virus. Applications arising from recent developments in the state of the art for transceiver and signal processing technologies will be discussed along associated technical implications. These applications include remote breathing rate monitoring, continuous identity authentication, occupancy detection, and hand gesture recognition. This paper also highlights future research directions that must be explored further to bring this innovative non-contact sensor technology into real-world implementation.

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Multiple Subject Respiratory Pattern Recognition and Estimation of Direction of Arrival using Phase-Comparison Monopulse Radar

Cited by 24 publications

References 5 publications

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Sensing of Life Activities at the Human-Microwave Frontier

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

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