An Intelligent Signal Processing Method for Motional Vital Signs Detection System Based on Deep Learning

Liu, Siyun; Qi, Qingjie; Xian, Wenhao; Zhang, Jingwen; Cheng, Haozhong; Liu, Yingjie; Ma, Tianfang; Wang, Yue; Li, Dan; Chai, Jiamei

doi:10.1109/access.2022.3211976

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…Overall, we can notice a tendency to increase the subject distance to 20 m, instead of working at the short range as in other applications. [193] employs MAE reporting an error of localisation ranging between 15 to 31 cm in outdoor conditions and 11 to 27 cm in indoor conditions; [194] employs RMSE with errors ranging from 12.3 cm to 20.7 cm; [196] uses as a metric the percentage of correct localisation within 30 cm or 10 cm error reporting a percentage from 2% to 15% with localisation based on breathing features only localisation and under 10% with localisation based on heartbeat features. All the included papers uses a UWB RADAR technology with two reported operating frequencies of 400 MHz and 4.3 GHz.…”

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

“…All the included papers uses a UWB RADAR technology with two reported operating frequencies of 400 MHz and 4.3 GHz. The used models are KNN [195] or variants [194], SVM [196], ELM [193] and a CNN [194]. Only in the case of Liu et al [194] subjects involved are localised while moving.…”

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

“…The used models are KNN [195] or variants [194], SVM [196], ELM [193] and a CNN [194]. Only in the case of Liu et al [194] subjects involved are localised while moving.…”

Section: B Monitoring Of Human Behaviourmentioning

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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Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

See 1 more Smart Citation

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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Remote Vital Sign Monitoring Based Millimeter-Wave Sensing

Zhao,

Zhang,

Yin

et al. 2024

IEEE Access

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Overview of Vivaldi Antenna Selection for Through-Wall Radar Applications

Amador,

Rouco,

Albuquerque

et al. 2024

Sensors

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This paper analyzes broadband antennas, with a special focus on Vivaldi antennas, for their suitability for through-wall radar applications. It assesses various antenna designs, emphasizing high gain, wide impedance bandwidth, and effective wall penetration capabilities. Vivaldi antennas are superior due to their broad bandwidth, high gain, and directional radiation patterns. This study further explores structural optimizations, feeding techniques, and performance enhancement strategies to refine Vivaldi antenna designs for through-wall radar systems. Through a comparative analysis and technical evaluation, this paper highlights Vivaldi antennas’ potential for improving through-wall radar systems’ imaging and sensing capabilities. This presents a pathway for future ultra-wideband advancements.

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An Intelligent Signal Processing Method for Motional Vital Signs Detection System Based on Deep Learning

Cited by 3 publications

References 33 publications

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

Remote Vital Sign Monitoring Based Millimeter-Wave Sensing

Overview of Vivaldi Antenna Selection for Through-Wall Radar Applications

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