UWB Radar Sensing for Respiratory Monitoring Exploiting Time- Frequency Spectrograms

Badshah, Syed Salman; Saeed, Umer; Momand, Asadullah; Shah, Syed Yaseen; Shah, Syed Ikram; Ahmad, Jawad; Abbasi, Qammer H.; Shah, Syed Aziz

doi:10.1109/smarttech54121.2022.00040

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Also the use of kappa statistic is reported in a single paper [147]. Abnormal breathing detection: We found five papers [112], [120], [131], [134], [139] dealing with the detection of abnormal versus normal breathing patterns in a binary classification scenario. From the point of view of complexity, it is a step beyond simply detecting apnea, because in the class of abnormal breathing we can find many different cases of abnormal breathing patterns, such as breathing associated with chronic obstructive pulmonary disease (COPD) [134].…”

Section: A Diagnosismentioning

confidence: 99%

“…From the point of view of complexity, it is a step beyond simply detecting apnea, because in the class of abnormal breathing we can find many different cases of abnormal breathing patterns, such as breathing associated with chronic obstructive pulmonary disease (COPD) [134]. In this case, we found the use of different models, where deep learning is the most diffused option (4 out of 5 papers) with solutions of complex CNN architectures, such as ResNet [112], [120], [131] ang VGG16 [120]. Transfer learning is employed in one paper as a method to simplify the training process [120].…”

Section: A Diagnosismentioning

confidence: 99%

“…([2023] [human detection] [UWB 7,29])[175]([2023] [heart disease detection] [UWB 4,3]);[173]([2016] [heart disease detection] [UWB]);[202]([2022] [affective computing] [FMCW 79]);[206]([2021] [affective computing] [UWB 7,29]);[134]([2022] [breath patterns classificaiton] [UWB 7,29]);[131]([2021] [breath patterns classificaiton] [UWB 7,25]);[128]([2022] [breath patterns classificaiton] [UWB 7,29]);[126]([2022] [breath patterns classificaiton] [UWB 7,29]);[124]([2020] [breath patterns classificaiton] [UWB 3,5] );[123]([2019] [breath patterns classificaiton] [UWB 3,5]);[38]([2019] [breath patterns classificaiton] [UWB 3,5]);[120]([2022] [breath patterns classificaiton] [UWB 7,29]);[148]([2023] [breath patterns classificaiton][62]);[117]([2022] [breath patterns classificaiton] [FMCW 78,5] )[112]([2022] [breath patterns classificaiton] [UWB 7,29])[200]([2023] [activities classification] [FMCW 78,5]) MLP (n=13) [183]([2023] [fatigue monitoring] [FMCW 79]); [172]([2022] [heart disease detection] [FMCW]); [151]([2021] [quality detection] [UWB 8,75]); [211]([2022] [user authentication] [FMCW 78]); [136]([2021] [breath patterns classification] [CW 24]); [184]([2021] [fatigue monitoring] [UWB 7,29]); [223]([2016] [user authentication] [CW]); [187]([2023] [human detection] [UWB 7,25]); [206]([2021] [affective computing] [UWB 7,29]); [181]([2021] [fatigue monitoring] [FMCW 78,5]); [122]([2018] [breath patterns classification] [UWB 6,8]); [15]([2022] [heart disease detection] [FMCW 79,4]); [120]([2022] [breath patterns classificaiton] [UWB 7,29]) CNN+RNN (n=4) [145]([2023] [breath patterns classificaiton] [FMCW 24]); [208]([2022] [affective computing] [FMCW 78,5]); [114]([2022] [breath patterns classificaiton] [FMCW 60 ]) RNN (n=4) [135]([2023] [breath patterns classificaiton] [UWB]); [149]([2023] [quality detection] [FMCW]); [169]([2021] [sleep s...…”

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confidence: 99%

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Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

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