Non-contact Blood Pressure Estimation Using a 300 GHz Continuous Wave Radar and Machine Learning Models

Jung, Marie; Caris, M.; Stanko, Stephan

doi:10.1109/memea52024.2021.9478734

Cited by 9 publications

(10 citation statements)

References 8 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%

“…From Table V, we can notice that the authors made an effort to make the dataset balanced between the two sex. The synchronisation between the RADAR recording and the reference is solved by different methods, such as Precision Time Protocol [30], [68], [94], custom LABVIEW program [73], [100], post-processing manual synchronisation based on clearly defined time events [107], binary shared sequences [78], [108] or by matching of the time stamps [17], [40], [65], [66], [72], [87]. Moreover, we find two methods used to deal with synchronisation errors.…”

Section: B Datasets General Informationmentioning

confidence: 99%

“…Regarding other metrics related to fidelity of signal reconstruction, we notice the diffusion of correlation [32], [36], [44], [63], also employed for HR analysis [69], and cosine similarity [30], [68], [94]. Another interesting metric related to signal fidelity is Noise, defined as difference between value estimation (n=13) [34], [40], [53], [54], [56], [58], [75], [86], [91]- [93], [100], [101]; signal reconstruction (n=8) [30], [32], [36], [41], [68], [69], [72], [94], normalised by heartbeat period (n=3) [36], [44],…”

Section: ) Performance Metricsmentioning

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%

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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: A First Cluster Tasksmentioning

confidence: 99%

Section: B Datasets General Informationmentioning

confidence: 99%

Section: ) Performance Metricsmentioning

confidence: 99%

Section: ) Performance Metricsmentioning

confidence: 99%

Section: ) Performance Metricsmentioning

confidence: 99%

See 3 more Smart Citations

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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Enhancing Privacy-Preserving Personal Identification Through Federated Learning With Multimodal Vital Signs Data

Hwang,

Shi,

Lee

2023

IEEE Access

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Personal identification can be achieved through various multimodal vital sign measurement methodologies in human physiology, such as electrocardiogram (ECG) and radar signals. However, this process inevitably brings privacy protection issues into light during data collection and analysis. Additionally, potential discomfort may be associated with the physical contact during these measurements. To mitigate these issues, our study explores the utilization of federated learning (FL) for privacy protection and non-contact sensors, such as radar, to alleviate contact-related discomfort. Our objective was to establish the viability of privacy-secured personal identification (PI) models that utilize FL. Furthermore, we scrutinized the performance of FL-based PI models that incorporate non-contact radar signals. We compared the performance levels of five conventional machine learning (ML) models with that of five FL-based models by utilizing ECG and radar signals. The experimental outcomes indicated that ECG-based models had superior accuracy overall, with the FL-CNN-BLSTM model yielding an accuracy of 97.3%. Models based on radar signals displayed slightly lower accuracy, with the FL-CNN-BLSTM model delivering an accuracy of 81.9%. These results confirmed the effectiveness of FL-based PI models employing radar signals. Our research therefore augments the evolution of privacy-guarded PI processes utilizing radar signals and FL and lays a robust groundwork for upcoming studies in this field.

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Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

Yoshizawa,

Yamamoto,

Ohtsuki

2023

ICC 2023 - IEEE International Conference on Communications

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Non-contact Blood Pressure Estimation Using a 300 GHz Continuous Wave Radar and Machine Learning Models

Cited by 9 publications

References 8 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

Enhancing Privacy-Preserving Personal Identification Through Federated Learning With Multimodal Vital Signs Data

Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

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