Number and Angle Analysis in UWB Radar Deployment for Vital Sign Monitoring

Yu, Yibo; Yang, Xiuzhu; Qian, Hongyu; Zhang, Xinyue; Li, Lei; Zhang, Lin

doi:10.1109/embc.2019.8857916

Cited by 2 publications

(6 citation statements)

References 10 publications

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“…Continuous vital signs monitoring can prevent life-threatening scenarios by providing early warnings before a decline of the patient's status [104]. In this sub-category of papers on value estimation we include papers dealing with the estimation of vital signs, such as HR (n = 21) [17], [22], [25], [28], [29], [52], [55]- [57], [62], [64], [74], [76], [82], [87], [91], [93], [96]- [98], BR [58], [66], [88], [89], [99], [102] (n = 6), both HR and BR [31], [34], [40], [53], [54], [92], [103] (n = 7), BP [65], [67], [75], [83], [85], [100], [101] (n = 7), oxygen saturation (SpO2) [84] (n = 1), and spyrometric indices [61] (n = 1) in a given windows of observation.…”

Section: A First Cluster Tasksmentioning

confidence: 99%

“…For the hybrid architecture the deep learning model used was an encoder-decoder type in combination with extracted features from K-nearest neighbour. The general [17], [22], [25], [28], [29], [31], [34], [40], [52]- [58], [61], [62], [64]- [67], [74]- [76], [82]- [89], [91]- [93], [96]- [103] Signal reconstruction (n = 18)…”

Section: A First Cluster Tasksmentioning

confidence: 99%

“…value estimation (n=13) [17], [25], [28], [29], [34], [57], [58], [62], [82], [84], [89], [101], [103]; physiological event detection (n=3) [59], [78], [79] MSE= N i (predictioni − truevaluei) 2 value estimation (n=5) [25], [56], [66], [84], [100]; physiological event detection (n=1) [59] RMSE=…”

Section: ) Performance Metricsmentioning

confidence: 99%

“…Augmentation is a necessary steps when there is a lack of real data and the complexity of the algorithms used for the regression or classification task necessitate an increased number of examples. The most typical augmentation procedures include segmentation of the recordings (n = 19) [17], [22], [28]- [30], [32], [34]- [36], [40], [41], [44], [55]- [57], [59], [63], [65], [66], [68], [69], [72]- [76], [78], [79], [81], [82], [87], [89], [91]- [96], [98]- [100], [102], [103], additive noise (n = 3) [29], [44], [96] and rotation of the I/Q data (n = 2) [69], [94]. The last row of reference processing refers to the elaboration of the reference signal needed to reduce the amount of information used in the training process.…”

Section: ) Performance Metricsmentioning

confidence: 99%

“…[53] Range Azimuth [58], [65], [99] Range Doppler [17], [29] Working with I/Q data [68], [69], [87], [94] Phase extraction (e.g. arctan) [22], [25], [28]- [30], [32], [34]- [36], [40], [41], [44], [55], [57], [59]- [61], [64]- [66], [70], [72]- [76], [78], [79], [81], [82], [85], [86], [89], [95]-[103] Unwrap [25], [52], [60], [64], [97] Envelope extraction [51], [78], [79] Normalisation or standardisation [23], [28], [51]- [53], [73] Filtering Bandpass or highpass filter w...…”

Section: Continuous Wavelet Transformmentioning

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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