Analysis of Spectral Estimation Algorithms for Accurate Heart Rate and Respiration Rate Estimation Using an Ultra-Wideband Radar Sensor

Hasan, Kareeb; Ebrahim, Malikeh Pour; Xu, Hongqiang; Yuce, Mehmet Rasit

doi:10.1109/rbme.2022.3212695

Cited by 9 publications

(15 citation statements)

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“…The test persons' positions relative to the radar were as follows: lying in 36 studies [2,3,5,13,17,23,28,29,[31][32][33][34]44,45,50,51,53,55,58,62,77,82,84,88,91,93,94,99,[104][105][106][107]110,112,114,120]; sitting in 60 studies [3,10,11,14-17,19,20,22,24,25,31,36,40,42,46-49,52-54,56,58-61,63,65,66,68-76, 78-80,83,85,86,88,89,96,97,100-103,109-111,113,117,118]; standing in 14 studies [3,21,26,27,39,57,72,76,81,87,110,113,…”

Section: Resultsmentioning

confidence: 99%

“…The radar used was positioned frontally to the test person in 81 studies [3,[10][11][12]15,16,[18][19][20][21][22][24][25][26][27][30][31][32][34][35][36][37][38][39][40]42,[44][45][46][47][48][49]52,53,[55][56][57][58][59][60][61][62][63]65,66,[68][69][70][71]73,75,76,…”

Section: Resultsmentioning

confidence: 99%

“…In five studies, radars with a central frequency in the range of <1 GHz were used [12,25,29,32,87]. In 57 studies, the radars employed used a central frequency in the range of 1-10 GHz [5,[10][11][12]15,16,19,20,22,24,27,[30][31][32]34,35,38,39,41,43,[46][47][48][49][51][52][53][54][55][57][58][59]68,69,83,[85][86][87][88][89][90][91][92][93][94]…”

Section: Resultsmentioning

confidence: 99%

“…In 94 studies, the reference measurement was performed using contact electrodes [2,3,[33][34][35][36]38,[40][41][42][43][44][45][46][47][48][49][50][51][53][54][55][57][58][59][61][62][63][64][65][66][68][69][70][73][74][75][76][77]79,80,[82][83][84]86,88,89,[93][94][95][96][97]…”

Section: Resultsmentioning

confidence: 99%

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Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

Liebetruth,

Kehe,

Steinritz

et al. 2024

Sensors

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The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

Liebetruth,

Kehe,

Steinritz

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In the paper by Islam et al [9] the machine learning signal processing approach is not considered. Other traditional signal processing techniques were evaluated recently in a review regarding IQ demodulation advances by Wang et al in 2022 [10] and in a review on spectral estimation algorithms by Hasan et al in 2022 [11]. In the last year, only four reviews are found regarding RADARbased physiological signal sensing [1], [2], [12]- [14], where a beginning interest in the machine learning applications is found.…”

Section: Introductionmentioning

confidence: 99%

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

Nocera,

Senigagliesi,

Raimondi

et al. 2024

IEEE Access

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Analysis of Spectral Estimation Algorithms for Accurate Heart Rate and Respiration Rate Estimation Using an Ultra-Wideband Radar Sensor

Cited by 9 publications

References 0 publications

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

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

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

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