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REVIEW 4 major objections 5 minor 88 references

UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Heart-rate features learned on 60 GHz FMCW radar transfer to 8 GHz IR-UWB radar, cutting UWB error from 5.4 to 4.1 bpm after fine-tuning.

desk verdict First FMCW-to-IR-UWB transfer result for heart rate is worth knowing about, but the 25% gain is not a clean estimate of transfer benefit because the baseline and transfer conditions differ by more than pretraining. read the letter →

arxiv 2507.14195 v1 pith:U6UQQG4L submitted 2025-07-14 eess.SP cs.AIcs.LG

Elzbieta Gruzewska , Pooja Rao , Sebastien Baur , Matthew Baugh , Mathias M.J. Bellaiche , Sharanya Srinivas , Octavio Ponce , Matthew Thompson
show 9 more authors
Pramod Rudrapatna Michael A. Sanchez Lawrence Z. Cai Timothy JA Chico Robert F. Storey Emily Maz Umesh Telang Shravya Shetty Mayank Daswani
This is my paper
classification eess.SPcs.AIcs.LG
keywords heartratemonitoringtransferlearningFMCWradarIR-UWBcontactlessvitalsignsresidualneuralnetworkrangeprofileconsumer
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that a model trained on one kind of radar can be reused for another, so bringing heart-rate monitoring to a new radar-equipped consumer device does not require collecting a huge paired dataset from scratch. The demonstration pairs a 60 GHz mm-wave FMCW radar model, trained on an overnight sleep dataset with 119 participants, with an 8 GHz impulse-radio ultra-wideband (IR-UWB) radar dataset collected from 376 participants in short sessions. After preprocessing the FMCW data to resemble IR-UWB data (one range bin, one antenna) and fine-tuning on the small IR-UWB set, the model reaches 4.1 bpm mean absolute error and 6.3% mean absolute percentage error, a 25% reduction over the 5.4 bpm from-scratch baseline and within the 5 bpm / 10% consumer-device accuracy threshold. The same architecture also halves the previous FMCW error on its original dataset, reaching 0.85 bpm mean absolute error. If these results hold, new radar systems could inherit accurate heart-rate models from existing ones instead of requiring their own large labeled collections.

What carries the argument

The load-bearing machinery is a 2D+1D ResNet operating on the magnitude and unwrapped angle of the complex radar range profile at the detected range bin. A first 2D ResNet processes the time-by-spatial-feature input to capture local spatio-temporal chest micromotion, the spatial dimension is then averaged away, and a 1D ResNet refines temporal features before a fully connected layer regresses heart rate. To make FMCW features transferable, the authors reduce FMCW data to one range bin and one receiving antenna, add Gaussian noise augmentation, and use an adaptive filter derived from the respiratory rate to remove breathing and its harmonics before the features enter the network.

What would settle it

Repeat the transfer with the pretraining FMCW data reduced by averaging ten range bins into one instead of selecting a single range bin, and check whether the fine-tuned IR-UWB MAE rises from 4.1 bpm toward the 5.4 bpm from-scratch baseline; if it does, the transfer gain depends on the single-bin approximation rather than on genuinely transferable cardiac features.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that transfer learning between radar modalities works for vital-sign measurement: features learned from the complex range profile of a 60 GHz FMCW radar remain useful when fine-tuned on an 8 GHz IR-UWB radar, even though the UWB radar has roughly ten times coarser range resolution and uses short pulses rather than chirps. The transferred model reaches a test MAE of 4.1 bpm and MAPE of 6.3% on IR-UWB data, compared with 5.4 bpm and 8.4% for the same architecture trained from scratch on IR-UWB data alone; the fine-tuned model also keeps a 97.5% recall. The paper further claims this meets the 5 bpm MAE / 10% MAPE accuracy standard for consumer heart-rate devices, and that the underlying 2D+1D ResNet architecture improves FMCW heart-rate accuracy to 0.85 bpm MAE, halving the previous reported error on that dataset. The authors present this as the first demonstration of transfer learning between FMCW and IR-UWB radar for heart-rate monitoring.

Load-bearing premise

The transfer works only if the cardiac micromotion seen by a single range bin and a single antenna of a 60 GHz FMCW radar is similar enough to what an 8 GHz IR-UWB radar sees, despite a tenfold coarser range resolution and different radar physics.

Editorial extensions

If this is right

  • A UWB chip already present in phones could measure heart rate contactlessly with an accuracy acceptable for consumer wellness devices, without collecting a large new labeled dataset for each phone's radar configuration.
  • New radar systems or hardware revisions could inherit heart-rate capability by fine-tuning an FMCW-pretrained model on a small dataset; the paper's ablation suggests the first 40% of the training set delivers most of the gain.
  • Combining unwrapped phase with magnitude matters for both radar types, and the 2D+1D structure reaches the same accuracy as a much larger 1D ResNet with roughly 15 times fewer parameters.
  • The transfer result is specific to the tested radar pair and configurations, so accuracy on other UWB integrations would need to be measured again.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I would expect the same single-range-bin, magnitude-plus-unwrapped-angle recipe to transfer heart-rate features between other radar pairs that share the complex range-profile representation, such as different FMCW bands or a UWB radar with a different bandwidth, though the paper only demonstrates one pair.
  • The fine-tuning curve saturating near 40% of the IR-UWB data suggests that data collection for a new radar could be substantially shorter than the 2-minute-per-position protocol used here, which is a testable consequence.
  • If the transferable representation really is the phase and magnitude of chest-wall micromotion, the same pretrained backbone may support heart-rate variability or pseudo-ECG/PPG reconstruction, directions the paper mentions as future work but does not test.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a 2D+1D ResNet architecture for radar-based heart rate estimation and investigates transfer learning between 60 GHz mm-wave FMCW radar and 8 GHz IR-UWB radar. On a large overnight FMCW dataset the model achieves a test MAE of 0.85 bpm and MAPE of 1.42%, halving the previously reported MAE of 1.69 bpm. For IR-UWB, the authors fine-tune a variant of the FMCW model after reducing both modalities to a single antenna and single range bin and applying Gaussian noise augmentation; the fine-tuned model reaches a test MAE of 4.1 bpm and MAPE of 6.3%, a 25% improvement over a from-scratch IR-UWB baseline at MAE 5.4 bpm. The paper includes extensive ablations on architecture, feature choice, antenna configuration, range-bin count, augmentation, and training-set size, with bootstrap confidence intervals.

Significance. If the transfer-learning effect is causal, this is a practically important result: it would show that a large FMCW radar dataset can reduce the data collection burden for a new radar modality, and it would support the feasibility of heart rate monitoring on UWB-enabled consumer devices. The paper's strengths are its large participant cohorts (119 participants for FMCW, 376 for IR-UWB), the use of held-out test sets with bootstrap confidence intervals, and the unusually thorough ablation program documented in the supplementary material. The main limitation is that the headline 25% improvement is not attributable to pretraining alone, because the transfer and baseline conditions differ in several simultaneous ways.

major comments (4)
  1. [§3.5.3, §3.5.4, Table 5] The central claim that FMCW pretraining causes the 25% MAE reduction is not established, because the transfer and baseline conditions differ by more than pretraining. The transfer pipeline uses a single antenna, swaps the order of magnitude and unwrapped-angle channels, and applies Gaussian noise augmentation during fine-tuning, whereas the from-scratch baseline is trained on antennas split into separate examples and no augmentation is described. Supplementary Table S10 shows that the feature flip plus Gaussian noise improves the fine-tuned model's validation MAE from 3.7 bpm to 3.2 bpm, so these choices have a measurable effect. A controlled comparison is needed that keeps preprocessing and augmentation identical for the transfer and from-scratch conditions, e.g., a from-scratch model trained with single-antenna input, feature swap, and Gaussian noise, or a transfer model fine-tuned without those augmentations. Without this, the reported 5.4-to-4.1 bpm improvement may be partially or entirely attributable to input configuration and augmentation rather than to knowledge transferred from FMCW pretraining.
  2. [§3.1.2, Tables 5–11] The IR-UWB test set excludes the upper-arm radar position, which was collected but removed from validation and test sets because the authors judged it out-of-distribution relative to FMCW data. Because the paper's headline IR-UWB accuracy is computed only on the two retained positions, this post-hoc exclusion materially narrows the claim. The manuscript should report performance including the upper-arm position, state whether the exclusion was decided before model selection, and justify why a deployment-relevant position should be omitted from evaluation.
  3. [§4.1, §3.1.1, Tables 2–4] The claimed halving of the previous FMCW state of the art (MAE 1.69 to 0.85 bpm) should be qualified with the evaluation protocol. The prior work reports performance under a detection-based protocol with a different recall level, and the present paper evaluates only on segments where presence detection succeeded (98.9% recall). The comparison would be cleaner if the paper reported the FMCW MAE under the same segment-inclusion and recall criteria as the prior work, or explicitly stated the protocol difference as a caveat. In addition, the high-HR range is nearly absent in the FMCW sleep dataset and sparsely represented in the IR-UWB dataset, so the subgroup MAEs for HR above 90 bpm should not be over-interpreted; the Discussion already acknowledges this, but the abstract-level 'maintained performance under 5 MAE/10% MAPE across heart rate ranges' should be tempered.
  4. [§3.5.3, §3.5.4, Supplementary Tables S7–S10] The design choices for the transfer pipeline (single antenna, single range bin, Gaussian noise parameters, feature-order swap, fine-tuning schedule) were selected based on validation-set performance on the IR-UWB target dataset. This validation-based selection tunes the method to the target distribution, which is legitimate but should be reported as such; the test set remains independent, but the reported confidence intervals do not account for the model-selection process. An additional concern is that the transfer premise—that a single FMCW range bin and a single IR-UWB range bin carry sufficiently similar cardiac micromotion information despite a 10x difference in range resolution—is supported only indirectly by the final accuracy. A direct analysis of feature similarity or a cross-modality sanity check would strengthen the claim that the transfer mechanism is the single-range-bin representation rather than a dataset-size or augmentation artifact.
minor comments (5)
  1. [§4.1] The text states 'total N=199 valid sessions' for the FMCW dataset, while §3.1.1 says 119 valid sessions and the abstract says N=119 participants; this numerical discrepancy should be corrected.
  2. [§3.5.4, §4.2] The baseline model is described as achieving an MAE of 2.450 bpm in §3.5.4 and 5.4 bpm on the test set in §4.2; the manuscript should state explicitly which number is the validation MAE and which is the test MAE to avoid confusion.
  3. [Supplementary Table S12] The final model's MAE on IR-UWB is reported as 3.8 bpm in the architecture ablation, while Table 5 reports a test MAE of 4.1 bpm; the supplementary table should clarify that the 3.8 bpm value is a validation-set result, not a test-set result.
  4. [§B.2.3, Supplementary Tables S9–S10] The feature-order swap is described as random during FMCW training in §B.2.3 but as a fixed swap during fine-tuning in §3.5.4; the manuscript should state precisely whether the fine-tuning augmentation swaps the order randomly per sample or always swaps it.
  5. [References] Reference [25] is incomplete ('The present and future of uwb'), and the supplementary material contains a typo, 'Gausian noise', which should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the transfer result is an empirical benchmark with independent test-set ground truth; the augmentation/feature-swap confound is an experimental validity issue, not a circular reduction.

full rationale

The central claims—FMCW MAE 0.85 bpm and transfer-learned IR-UWB MAE 4.1 bpm versus a from-scratch baseline of 5.4 bpm—are evaluated on held-out test segments against ECG/PPG ground truth, not derived from the model inputs or from a fitted parameter. The pretrained and baseline models share the same 2D+1D ResNet architecture, and the comparison is a direct empirical benchmark. Self-citations appear ([19] provides the FMCW dataset and prior SOTA; [31] supplies the adaptive respiratory filter), but neither is load-bearing for the transfer conclusion: the dataset is external and the filter is applied to both radar conditions. The main methodological weakness is that the transfer pipeline includes single-antenna selection, feature-order swapping, and Gaussian-noise augmentation that the from-scratch baseline does not receive; Supplementary Table S10 shows augmentations alone reduce validation MAE from 3.7 to 3.2 bpm for the fine-tuned model. This is a confounding-variable threat to the causal attribution of the gain to FMCW pretraining, not a circularity: no equation or prediction reduces by construction to its own input, and the reported test numbers are not statistically forced by the design choices. Hence no circular step is identified.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central empirical result rests on domain assumptions about radar signal representations and on a set of validation-selected preprocessing choices; no new physical entities or mathematical constants are introduced.

free parameters (5)
  • Feature-order swap during fine-tuning = enabled
    Chosen from ablation B.2.3: swapping magnitude and unwrapped angle order during fine-tuning reduces validation MAE from 3.7 to 3.4 (with Gaussian noise to 3.2); the reported test MAE depends on this choice.
  • Gaussian noise augmentation parameters = std=0.0005, probability 0.7 pretraining / 0.6 fine-tuning
    Selected via ablations in B.2.3; improves transfer model over no augmentation and is included in the final transfer recipe.
  • Single range bin selection = 1
    Ablation Table S6 showed 1 range bin optimal for UWB baseline; also used for transfer to match range resolution between modalities.
  • Antenna selection = FMCW antenna #2, UWB antenna #0
    Ablations Tables S7 and S8 showed best fine-tuned UWB MAE with FMCW antenna #2 pretraining and UWB antenna #0 fine-tuning.
  • Fine-tuning hyperparameters = lr=0.0003, decay=0.1, batch=2048, 2.7k steps
    Chosen for the final model; standard hyperparameter selection, not physically constrained, but affects the reported 4.1 bpm test MAE.
assumptions (4)
  • domain assumption Radar range-profile magnitude and unwrapped phase at a selected range bin encode cardiac chest-wall micromotion after clutter removal and adaptive respiratory filtering.
    Used throughout Sections 3.4 and 3.5; no independent validation that the extracted single-bin representation isolates cardiac motion.
  • ad hoc to paper The cardiac micromotion signal in a single FMCW range bin and a single IR-UWB range bin is similar enough for feature transfer despite 10x range-resolution difference.
    Introduced in Section 3.5.3 without a physical derivation; if false, the transfer result does not generalize.
  • domain assumption The CFAR detector with threshold 1.5 identifies the correct range bin containing the participant.
    Section 3.3; failure of this step is acknowledged by the authors as the primary error source in Section 5.1.
  • domain assumption ECG- or PPG-derived ground-truth heart rates are accurate for kept segments after R-peak noise rejection.
    Sections 3.1.1 and 3.1.2; rejection rates of 10-17% create potential for selective labeling, though this is standard for the literature.

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Cite this review

Pith. "Pith review of UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach." pith.science (2026). https://pith.science/paper/U6UQQG4L

@misc{pith2026250714195,
  author       = {Pith},
  title        = {Pith review of: UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U6UQQG4L}},
  note         = {Machine review of arXiv:2507.14195}
}
read the original abstract

Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices.

Figures

Figures reproduced from arXiv: 2507.14195 by the authors.

Figure 1
Figure 1. Three radar positions used during the IR-UWB data collection study. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. The high-level architecture of the system, showing the signal processing, presence detection and feature [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Flattening of the mm-wave FMCW data that is performed in the Input Layer. Features that are spatially [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Novel model architecture which combines a 2D and 1D ResNet network. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: High level architecture of model transfer. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Bland-Altman analysis to assess the agreement between predicted HR and the ground truth HR values on [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: a) Representative example of overnight session performance on the test set. b) An example of the session [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Bland-Altman analysis to assess the agreement between predicted HR the ground truth HR values on IR-UWB [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: a), b) Representative examples of the model performance (blue) compared to the ground truth (orange) for 3 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

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Works this paper leans on

88 extracted references · 79 canonical work pages

  1. [37]

    Ultra-wideband radar for simultaneous and unobtrusive monitoring of respiratory and heart rates in early childhood: A deep transfer learning approach

    Emad Arasteh, Esther S Veldhoen, Xi Long, Maartje van Poppel, Marjolein van der Linden, Thomas Alderliesten, Joppe Nijman, Robbin de Goederen, and Jeroen Dudink. Ultra-wideband radar for simultaneous and unobtrusive monitoring of respiratory and heart rates in early childhood: A deep transfer learning approach. Sensors, 23(18):7665, 2023

    work page 2023

  2. [1]

    Importance of resting heart rate.Trends in Cardiovascular Medicine, 33(8):502–515, 2023

    Brian Olshansky, Fabrizio Ricci, and Artur Fedorowski. Importance of resting heart rate.Trends in Cardiovascular Medicine, 33(8):502–515, 2023

    2023

  3. [2]

    Association between heart rate and mortality in patients with septic shock: an analysis revealed by time series data

    Yi-Le Ning, Wen-Jia Li, Xiang Lu, Yu Zhang, Jun-Wei Zhang, and Ji-Hong Zhou. Association between heart rate and mortality in patients with septic shock: an analysis revealed by time series data. BMC Infectious Diseases, 24(1):1088, 2024

    2024

  4. [3]

    Rest heart rate and life expectancy

    Herbert J Levine et al. Rest heart rate and life expectancy. Journal of the American College of Cardiology , 30(4):1104–1106, 1997

    1997

  5. [4]

    Thyrotoxicosis

    Brannon L Inman and Brit Long. Thyrotoxicosis. Emergency medicine clinics of North America, 41(4):759–774, 2023

    2023

  6. [5]

    Pathophysiology of heart failure

    Robert HG Schwinger. Pathophysiology of heart failure. Cardiovascular diagnosis and therapy, 11(1):263, 2021

    2021

  7. [6]

    Central circuitries for body temperature regulation and fever.American journal of Physiology- Regulatory, integrative and comparative Physiology, 301(5):R1207–R1228, 2011

    Kazuhiro Nakamura. Central circuitries for body temperature regulation and fever.American journal of Physiology- Regulatory, integrative and comparative Physiology, 301(5):R1207–R1228, 2011

    2011

  8. [7]

    Sinus tachycardia: a multidisciplinary expert focused review

    Kenneth A Mayuga, Artur Fedorowski, Fabrizio Ricci, Rakesh Gopinathannair, Jonathan Walter Dukes, Christo- pher Gibbons, Peter Hanna, Dan Sorajja, Mina Chung, David Benditt, et al. Sinus tachycardia: a multidisciplinary expert focused review. Circulation: Arrhythmia and Electrophysiology, 15(9):e007960, 2022

    2022

Show all 88 references
  1. [8]

    Use and limitations of clinical and radiologic diagnosis of pneumonia

    Matthew Mabie and Richard G Wunderink. Use and limitations of clinical and radiologic diagnosis of pneumonia. In Seminars in respiratory infections, volume 18, pages 72–79. [Orlando, Fla.]: Grune & Stratton, c1986-2003., 2003

    2003

  2. [9]

    Predictors of pneumonia in lower respiratory tract infections: 3c prospective cough complication cohort study

    Michael Moore, Beth Stuart, Paul Little, Sue Smith, Matthew J Thompson, Kyle Knox, Anne van den Bruel, Mark Lown, and David Mant. Predictors of pneumonia in lower respiratory tract infections: 3c prospective cough complication cohort study. European respiratory journal, 50(5), 2017

    2017

  3. [10]

    Water as an essential nutrient: the physiological basis of hydration

    Eric Jéquier and Florence Constant. Water as an essential nutrient: the physiological basis of hydration. European journal of clinical nutrition, 64(2):115–123, 2010

    2010

  4. [11]

    Detecting sleep outside the clinic using wearable heart rate devices

    Ignacio Perez-Pozuelo, Marius Posa, Dimitris Spathis, Kate Westgate, Nicholas Wareham, Cecilia Mascolo, Søren Brage, and Joao Palotti. Detecting sleep outside the clinic using wearable heart rate devices. Scientific Reports, 12(1):7956, 2022

    2022

  5. [12]

    An examination and critique of current methods to determine exercise intensity

    Nicholas A Jamnick, Robert W Pettitt, Cesare Granata, David B Pyne, and David J Bishop. An examination and critique of current methods to determine exercise intensity. Sports Medicine, 50(10):1729–1756, 2020

    2020

  6. [13]

    Assessment of ownership of smart devices and the acceptability of digital health data sharing

    Md Mobashir Hasan Shandhi, Karnika Singh, Natasha Janson, Perisa Ashar, Geetika Singh, Baiying Lu, D Sun- shine Hillygus, Jennifer M Maddocks, and Jessilyn P Dunn. Assessment of ownership of smart devices and the acceptability of digital health data sharing. NPJ digital medici...

    2024

  7. [14]

    A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults

    Dean J Miller, Charli Sargent, and Gregory D Roach. A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults. Sensors, 22(16):6317, 2022

    2022

  8. [15]

    Real-time through-wall imaging using an ultrawideband multiple-input multiple-output (mimo) phased array radar system

    Tyler S Ralston, Gregory L Charvat, and John E Peabody. Real-time through-wall imaging using an ultrawideband multiple-input multiple-output (mimo) phased array radar system. In 2010 IEEE international symposium on phased array systems and technology, pages 551–558. IEEE, 2010

    2010

  9. [16]

    Non-contact heart rate monitoring based on millimeter wave radar

    Zongquan Ling, Weinan Zhou, Yuhao Ren, Jiping Wang, and Liquan Guo. Non-contact heart rate monitoring based on millimeter wave radar. IEEE Access, 10:74033–74044, 2022

    2022

  10. [17]

    Vital sign monitoring using fmcw radar in various sleeping scenarios

    Emmi Turppa, Juha M Kortelainen, Oleg Antropov, and Tero Kiuru. Vital sign monitoring using fmcw radar in various sleeping scenarios. Sensors, 20(22):6505, 2020

    2020

  11. [18]

    Non-contact vital signs monitoring for multiple subjects using a millimeter wave fmcw automotive radar

    Shekh MM Islam, Naoyuki Motoyama, Sergio Pacheco, and Victor M Lubecke. Non-contact vital signs monitoring for multiple subjects using a millimeter wave fmcw automotive radar. In 2020 IEEE/MTT-S International Microwave Symposium (IMS), pages 783–786. IEEE, 2020

    2020

  12. [19]

    Soli-enabled noncontact heart rate detection for sleep and meditation tracking

    Luzhou Xu, Jaime Lien, Haiguang Li, Nicholas Gillian, Rajeev Nongpiur, Jihan Li, Qian Zhang, Jian Cui, David Jorgensen, Adam Bernstein, et al. Soli-enabled noncontact heart rate detection for sleep and meditation tracking. Scientific Reports, 13(1):18008, 2023. 17

    2023

  13. [20]

    Ecg monitoring system integrated with ir-uwb radar based on cnn

    Wenfeng Yin, Xiuzhu Yang, Lin Zhang, and Eiji Oki. Ecg monitoring system integrated with ir-uwb radar based on cnn. IEEE Access, 4:6344–6351, 2016

    2016

  14. [21]

    A uwb radar-based approach of detecting vital signals

    Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, and Giancarlo Fortino. A uwb radar-based approach of detecting vital signals. In 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pages 1–4. IEEE, 2021

    2021

  15. [22]

    A novel non-contact heart rate monitor using impulse-radio ultra-wideband (ir-uwb) radar technology

    Yonggu Lee, Jun-Young Park, Yeon-Woo Choi, Hyun-Kyung Park, Seok-Hyun Cho, Sung Ho Cho, and Young- Hyo Lim. A novel non-contact heart rate monitor using impulse-radio ultra-wideband (ir-uwb) radar technology. Scientific reports, 8(1):13053, 2018

    2018

  16. [23]

    A comprehensive overview on uwb radar: Applications, standards, signal processing techniques, datasets, radio chips, trends and future research directions

    Mohammad Cheraghinia, Adnan Shahid, Stijn Luchie, Gert-Jan Gordebeke, Olivier Caytan, Jaron Fontaine, Ben Van Herbruggen, Sam Lemey, and Eli De Poorter. A comprehensive overview on uwb radar: Applications, standards, signal processing techniques, datasets, radio chips, trends ...

    2024

  17. [24]

    Unleashing the potential of uwb: Regulatory considerations

    FiRa Consortium. Unleashing the potential of uwb: Regulatory considerations. White paper, FiRa Consortium, August 2022. Accessed on 2025-06-24

    2022

  18. [25]

    The present and future of uwb

  19. [26]

    Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

    Sinan Gezici, Zhi Tian, Georgios B Giannakis, Hisashi Kobayashi, Andreas F Molisch, H Vincent Poor, and Zafer Sahinoglu. Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE signal processing magazine, 22(4):70–84, 2005

    2005

  20. [27]

    Surface chest motion decomposition for cardiovascular monitoring

    Ghufran Shafiq and Kalyana C Veluvolu. Surface chest motion decomposition for cardiovascular monitoring. Scientific reports, 4(1):5093, 2014

    2014

  21. [28]

    Ir-uwb radar-based robust heart rate detection using a deep learning technique intended for vehicular applications

    Faheem Khan, Stéphane Azou, Roua Youssef, Pascal Morel, and Emanuel Radoi. Ir-uwb radar-based robust heart rate detection using a deep learning technique intended for vehicular applications. Electronics, 11(16):2505, 2022

    2022

  22. [29]

    Heart rate variability monitoring based on doppler radar using deep learning

    Sha Yuan, Shaocan Fan, Zhenmiao Deng, and Pingping Pan. Heart rate variability monitoring based on doppler radar using deep learning. Sensors, 24(7):2026, 2024

    2026

  23. [30]

    Non-contact heart rate detection technology based on deep learning

    Hongfei Li, Yunxue Liu, Min Zhou, Zhen Cao, Xiao Zhai, and Yan Zhang. Non-contact heart rate detection technology based on deep learning. In 2023 International Seminar on Computer Science and Engineering Technology (SCSET), pages 272–277. IEEE, 2023

    2023

  24. [31]

    Uwb radar cardiac activity sensing: Novel arctangent demodulator for direct-rf receivers

    Sharanya Srinivas, Yu Rong, and Daniel W Bliss. Uwb radar cardiac activity sensing: Novel arctangent demodulator for direct-rf receivers. In 2020 IEEE International Radar Conference (RADAR), pages 984–989. IEEE, 2020

    2020

  25. [32]

    Heart rate sensing with a robot mounted mmwave radar

    Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang, Changhao Chen, Linhai Xie, Mengyu Wang, Niki Trigoni, and Andrew Markham. Heart rate sensing with a robot mounted mmwave radar. In2020 IEEE International Conference on Robotics and Automation (ICRA), pages 2812–2818. IEEE, 2020

    2020

  26. [33]

    Precise heart rate measurement using non-contact doppler radar assisted by machine-learning-based sleep posture estimation

    Kotaro Higashi, Guanghao Sun, and Koichiro Ishibashi. Precise heart rate measurement using non-contact doppler radar assisted by machine-learning-based sleep posture estimation. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Societ...

    2019

  27. [34]

    A novel heart rate detection algorithm with small observing window using millimeter-wave radar

    Jiacheng Wu, Han Cui, and Naim Dahnoun. A novel heart rate detection algorithm with small observing window using millimeter-wave radar. In 2022 11th Mediterranean Conference on Embedded Computing (MECO), pages 1–4. IEEE, 2022

    2022

  28. [35]

    Millimeter wave radar calibration for heart rate estimation using bayesian neural networks

    Felipe Parralejo, José A Paredes, Fernando J Aranda, Fernando J Álvarez, and África Vicario. Millimeter wave radar calibration for heart rate estimation using bayesian neural networks. In 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pages 1–6. ...

    2023

  29. [36]

    Development of predictive cnn based model for vital signs alerts

    RF Gibadullin and NS Marushkai. Development of predictive cnn based model for vital signs alerts. In 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pages 404–409. IEEE, 2021

    2021

  30. [38]

    Exercise vital signs detection employing fmcw radar and artificial neural networks

    King Leong Li, Shih-Hsuan Lai, Kyle Cheng, Lindor Henrickson, Irwin Chen, Vincent Wu, and Jyehong Chen. Exercise vital signs detection employing fmcw radar and artificial neural networks. InCLEO: QELS_Fundamental Science, pages JW1A–149. Optica Publishing Group, 2021. 18

    2021

  31. [39]

    Convolutional neural networks for the real-time monitoring of vital signs based on impulse radio ultrawide-band radar during sleep

    Sang Ho Choi and Heenam Yoon. Convolutional neural networks for the real-time monitoring of vital signs based on impulse radio ultrawide-band radar during sleep. Sensors, 23(6):3116, 2023

    2023

  32. [40]

    Heartbeatnet: Enhancing fast and accurate heart rate estimation with fmcw radar and lightweight deep learning

    Dongryul Kim, Jaeyoung Choi, Joonseok Yoon, Sungpil Cheon, and Byungkwan Kim. Heartbeatnet: Enhancing fast and accurate heart rate estimation with fmcw radar and lightweight deep learning. IEEE Sensors Letters, 2024

    2024

  33. [41]

    Physical activity monitoring for heart rate (ansi/cta-2065)

    JA Consumer Technology Association et al. Physical activity monitoring for heart rate (ansi/cta-2065). American National Standards, 2018

    2018

  34. [42]

    Standard, Association for the Advancement of Medical Instrumentation, Arlington, Virginia, USA, 2016

    Medical electrical equipment - Part 2-27: Particular requirements for the basic safety and essential perfor- mance of electrocardiographic monitoring equipment. Standard, Association for the Advancement of Medical Instrumentation, Arlington, Virginia, USA, 2016

    2016

  35. [43]

    Standard, Association for the Advancement of Medical Instrumentation, Arlington, Virginia, USA, 2002

    Cardiac Monitors, Heart Rate Meters, and Alarms. Standard, Association for the Advancement of Medical Instrumentation, Arlington, Virginia, USA, 2002

    2002

  36. [44]

    Monitoring long-term cardiac activity with contactless radio frequency signals

    Bin-Bin Zhang, Dongheng Zhang, Yadong Li, Zhi Lu, Jinbo Chen, Haoyu Wang, Fang Zhou, Yu Pu, Yang Hu, Li-Kun Ma, et al. Monitoring long-term cardiac activity with contactless radio frequency signals. Nature Communications, 15(1):1–11, 2024

    2024

  37. [45]

    Local pulse wave detection using continuous wave radar systems.IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 1(2):81–89, 2017

    Christoph Will, Kilin Shi, Sven Schellenberger, Tobias Steigleder, Fabian Michler, Robert Weigel, Christoph Ostgathe, and Alexander Koelpin. Local pulse wave detection using continuous wave radar systems.IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biolo...

    2017

  38. [46]

    Cuffless blood pressure estimation from the carotid pulse arrival time using continuous wave radar

    Dilpreet Buxi, Jean-Michel Redouté, and Mehmet Rasit Yuce. Cuffless blood pressure estimation from the carotid pulse arrival time using continuous wave radar. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 570...

    2015

  39. [47]

    Ecg signal reconstruction using fmcw radar and a convolutional neural network for contactless vital-sign sensing

    Daiki Toda, Ren Anzai, Koichi Ichige, Ryo Saito, and Daichi Ueki. Ecg signal reconstruction using fmcw radar and a convolutional neural network for contactless vital-sign sensing. IEICE Transactions on Communications, 106(1):65–73, 2023

    2023

  40. [48]

    Contactless electrocardiogram monitoring with millimeter wave radar

    Jinbo Chen, Dongheng Zhang, Zhi Wu, Fang Zhou, Qibin Sun, and Yan Chen. Contactless electrocardiogram monitoring with millimeter wave radar. IEEE Transactions on Mobile Computing, 23(1):270–285, 2022

    2022

  41. [49]

    Machine learning in radar-based physiological signals sensing: a scoping review of the models, datasets and metrics

    Antonio Nocera, Linda Senigagliesi, Michela Raimondi, Gianluca Ciattaglia, and Ennio Gambi. Machine learning in radar-based physiological signals sensing: a scoping review of the models, datasets and metrics. IEEE Access, 2024

    2024

  42. [50]

    Dl-aided nomp: A deep learning-based vital sign estimating scheme using fmcw radar

    Hsin-Yuan Chang, Chia-Hung Lin, Yu-Chien Lin, Wei-Ho Chung, and Ta-Sung Lee. Dl-aided nomp: A deep learning-based vital sign estimating scheme using fmcw radar. In 2020 IEEE 91st vehicular technology conference (VTC2020-Spring), pages 1–7. IEEE, 2020

    2020

  43. [51]

    Here: Heartbeat signal reconstruction for low-power millimeter-wave radar based on deep learning

    Haili Wang, Fuchuan Du, Hao Zhu, Zhuangzhuang Zhang, Yizhao Wang, Qixin Cao, and Xiaoxiao Zhu. Here: Heartbeat signal reconstruction for low-power millimeter-wave radar based on deep learning. IEEE Transactions on Instrumentation and Measurement, 72:1–15, 2023

    2023

  44. [52]

    Fast acquisition and accurate vital sign estima- tion with deep learning-aided weighted scheme using fmcw radar

    Hsin-Yuan Chang, Chih-Hsuan Hsu, and Wei-Ho Chung. Fast acquisition and accurate vital sign estima- tion with deep learning-aided weighted scheme using fmcw radar. In 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring), pages 1–6. IEEE, 2022

    2022

  45. [53]

    Interference motion removal for doppler radar vital sign detection using variational encoder-decoder neural network

    Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie, Ivan Andonovic, and Christos Tachtatzis. Interference motion removal for doppler radar vital sign detection using variational encoder-decoder neural network. In 2021 IEEE Radar Conference (RadarConf21), pag...

    2021

  46. [54]

    Spectral viterbi algorithm for contactless wide-range heart rate estimation with deep clustering

    Chen Ye and Tomoaki Ohtsuki. Spectral viterbi algorithm for contactless wide-range heart rate estimation with deep clustering. IEEE Transactions on Microwave Theory and Techniques, 69(5):2629–2641, 2021

    2021

  47. [55]

    Rf vital sign sensing under free body movement

    Jian Gong, Xinyu Zhang, Kaixin Lin, Ju Ren, Yaoxue Zhang, and Wenxun Qiu. Rf vital sign sensing under free body movement. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(3):1–22, 2021

    2021

  48. [56]

    An efficient heart rate measurement system using medical radar and lstm neural network

    Thanh Han-Trong and Hoang Nguyen Viet. An efficient heart rate measurement system using medical radar and lstm neural network. Journal of Electrical and Computer Engineering, 2022(1):4696163, 2022

    2022

  49. [57]

    A non-contact heart rate measurement system based on lstm neural network

    Thanh Han Trong and Hoang Nguyen Viet. A non-contact heart rate measurement system based on lstm neural network. In 2022 7th National Scientific Conference on Applying New Technology in Green Buildings (ATiGB), pages 205–210. IEEE, 2022

    2022

  50. [58]

    Fedradar: Federated multi-task transfer learning for radar-based internet of medical things

    Xikang Jiang, Jinhui Zhang, and Lin Zhang. Fedradar: Federated multi-task transfer learning for radar-based internet of medical things. IEEE Transactions on Network and Service Management, 20(2):1459–1469, 2023. 19

    2023

  51. [59]

    High accuracy respiration and heart rate detection based on artificial neural network regression

    Yu-Chiao Tsai, Shih-Hsuan Lai, Ching-Ju Ho, Fang-Ming Wu, Lindor Henrickson, Chia-Chien Wei, Irwin Chen, Vincent Wu, and Jyehong Chen. High accuracy respiration and heart rate detection based on artificial neural network regression. In 2020 42nd Annual International Conference...

    2020

  52. [60]

    Indoor activity and vital sign monitoring for moving people with multiple radar data fusion

    Xiuzhu Yang, Xinyue Zhang, Yi Ding, and Lin Zhang. Indoor activity and vital sign monitoring for moving people with multiple radar data fusion. Remote Sensing, 13(18):3791, 2021

    2021

  53. [61]

    Contactless heart and respiration rates estimation and classification of driver physiological states using cw radar and temporal neural networks

    Amal El Abbaoui, David Sodoyer, and Fouzia Elbahhar. Contactless heart and respiration rates estimation and classification of driver physiological states using cw radar and temporal neural networks. Sensors, 23(23):9457, 2023

    2023

  54. [62]

    Mae-based self-supervised pretraining algorithm for heart rate estimation of radar signals

    Yashan Xiang, Jian Guo, Ming Chen, Zheyu Wang, and Chong Han. Mae-based self-supervised pretraining algorithm for heart rate estimation of radar signals. Sensors, 23(18):7869, 2023

    2023

  55. [63]

    Heartbeat information prediction based on transformer model using millimetre-wave radar

    Bojun Hu, Biao Jin, Hao Xue, Zhenkai Zhang, Zhaoyang Xu, and Xiaohua Zhu. Heartbeat information prediction based on transformer model using millimetre-wave radar. IET Biometrics, 12(4):235–243, 2023

    2023

  56. [64]

    Contactless radar heart rate variability monitoring via deep spatio-temporal modeling

    Haoyu Wang, Jinbo Chen, Dongheng Zhang, Zhi Lu, Changwei Wu, Yang Hu, Qibin Sun, and Yan Chen. Contactless radar heart rate variability monitoring via deep spatio-temporal modeling. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (I...

    2024

  57. [65]

    A closer look at spatiotemporal convolutions for action recognition

    Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6450–6459, 2018

    2018

  58. [66]

    Digit recognition using fmcw and uwb radar sensors: A transfer learning approach

    Jaehoon Jung, Sohee Lim, Jihye Kim, and Seong-Cheol Kim. Digit recognition using fmcw and uwb radar sensors: A transfer learning approach. IEEE Sensors Journal, 23(16):18776–18784, 2023

    2023

  59. [67]

    Ultra-wideband data as input of a combined efficientnet and lstm architecture for human activity recognition

    Alexandre Beaulieu, Florentin Thullier, Kévin Bouchard, Julien Maître, and Sébastien Gaboury. Ultra-wideband data as input of a combined efficientnet and lstm architecture for human activity recognition. Journal of Ambient Intelligence and Smart Environments, 14(3):157–172, 2022

    2022

  60. [68]

    Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks

    Jinhee Park, Rios Jesus Javier, Taesup Moon, and Youngwook Kim. Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks. Sensors, 16(12):1990, 2016

    1990

  61. [69]

    Supervised domain adaptation for few-shot radar-based human activity recognition

    Xinyu Li, Yuan He, J Andrew Zhang, and Xiaojun Jing. Supervised domain adaptation for few-shot radar-based human activity recognition. IEEE Sensors Journal, 21(22):25880–25890, 2021

    2021

  62. [70]

    Unsupervised domain adaptation for micro-doppler human motion classification via feature fusion

    Yue Lang, Qing Wang, Yang Yang, Chunping Hou, Danyang Huang, and Wei Xiang. Unsupervised domain adaptation for micro-doppler human motion classification via feature fusion. IEEE Geoscience and Remote Sensing Letters, 16(3):392–396, 2018

    2018

  63. [71]

    mm-wave radar-based vital signs monitoring and arrhythmia detection using machine learning

    Srikrishna Iyer, Leo Zhao, Manoj Prabhakar Mohan, Joe Jimeno, Mohammed Yakoob Siyal, Arokiaswami Alphones, and Muhammad Faeyz Karim. mm-wave radar-based vital signs monitoring and arrhythmia detection using machine learning. Sensors, 22(9):3106, 2022

    2022

  64. [72]

    A supervised machine learning algorithm for heart-rate detection using doppler motion-sensing radar

    Justin Saluja, Joaquin Casanova, and Jenshan Lin. A supervised machine learning algorithm for heart-rate detection using doppler motion-sensing radar. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 4(1):45–51, 2019

    2019

  65. [73]

    Noncontact heart rate measurement based on an improved convolutional sparse coding method using ir-uwb radar

    Pengfei Wang, Fugui Qi, Miao Liu, Fulai Liang, Huijun Xue, Yang Zhang, Hao Lv, and Jianqi Wang. Noncontact heart rate measurement based on an improved convolutional sparse coding method using ir-uwb radar. IEEE Access, 7:158492–158502, 2019

    2019

  66. [74]

    Accurate heart rate and respiration rate detection based on a higher-order harmonics peak selection method using radar non-contact sensors

    Hongqiang Xu, Malikeh P Ebrahim, Kareeb Hasan, Fatemeh Heydari, Paul Howley, and Mehmet Rasit Yuce. Accurate heart rate and respiration rate detection based on a higher-order harmonics peak selection method using radar non-contact sensors. Sensors, 22(1):83, 2021

    2021

  67. [75]

    Fundamentals of radar signal processing, volume 1

    Mark A Richards et al. Fundamentals of radar signal processing, volume 1. Mcgraw-hill New York, 2005

    2005

  68. [76]

    Experimental comparison of ir-uwb radar and fmcw radar for vital signs

    Dingyang Wang, Sungwon Yoo, and Sung Ho Cho. Experimental comparison of ir-uwb radar and fmcw radar for vital signs. Sensors, 20(22):6695, 2020

    2020

  69. [77]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017

    2017 arXiv

  70. [78]

    Understanding bland altman analysis

    Davide Giavarina. Understanding bland altman analysis. Biochemia medica, 25(2):141–151, 2015

    2015

  71. [79]

    Association between arterial stiffness and blood pressure progression with incident hypertension: a systematic review and meta-analysis

    Alicia Saz-Lara, Rosa María Bruno, Iván Cavero-Redondo, Celia Álvarez-Bueno, Blanca Notario-Pacheco, and Vicente Martínez-Vizcaíno. Association between arterial stiffness and blood pressure progression with incident hypertension: a systematic review and meta-analysis. Frontier...

    2022

  72. [80]

    Developing technologies to assess vascular ageing: a roadmap from vascagenet

    Serena Zanelli, Davide Agnoletti, Jordi Alastruey, John Allen, Elisabetta Bianchini, Vasiliki Bikia, Pierre Boutouyrie, Rosa Maria Bruno, Rachel Climie, Djammaleddine Djeldjli, et al. Developing technologies to assess vascular ageing: a roadmap from vascagenet. Physiological M...

    2024

  73. [81]

    Association of arterial stiffness and neuropathy in diabetes: a systematic review and meta-analysis

    Angela Beros, John Sluyter, and Robert Keith Rhodes Scragg. Association of arterial stiffness and neuropathy in diabetes: a systematic review and meta-analysis. BMJ Open Diabetes Research and Care, 11(1):e003140, 2023

    2023

  74. [82]

    Performance of triage systems in emergency care: a systematic review and meta-analysis

    Joany M Zachariasse, Vera van der Hagen, Nienke Seiger, Kevin Mackway-Jones, Mirjam van Veen, and Henriette A Moll. Performance of triage systems in emergency care: a systematic review and meta-analysis. BMJ open, 9(5):e026471, 2019

    2019

  75. [83]

    Vital sign assessment

    Amit Sapra, Ahmad Malik, and Priyanka Bhandari. Vital sign assessment. In StatPearls [internet]. StatPearls Publishing, 2023

    2023

  76. [84]

    Physiologic predictors of severe injury: systematic review

    Annette Marie Totten, Tamara P Cheney, Maya E O’Neil, Craig D Newgard, Mohamud Daya, Rongwei Fu, Ngoc Wasson, Erica L Hart, and Roger Chou. Physiologic predictors of severe injury: systematic review. 2019

    2019

  77. [85]

    Emergency department triage

    Charles C Yancey and Maria C O’Rourke. Emergency department triage. 2020

    2020

  78. [86]

    Diagnostic value of clinical features at presentation to identify serious infection in children in developed countries: a systematic review

    Ann Van den Bruel, Tanya Haj-Hassan, Matthew Thompson, Frank Buntinx, and David Mant. Diagnostic value of clinical features at presentation to identify serious infection in children in developed countries: a systematic review. The Lancet, 375(9717):834–845, 2010

    2010

  79. [87]

    Improving the prioritization of children at the emergency department: Updating the manchester triage system using vital signs

    Joany M Zachariasse, Ian K Maconochie, Ruud G Nijman, Susanne Greber-Platzer, Frank J Smit, Daan Nieboer, Johan van der Lei, Claudio F Alves, and Henriette A Moll. Improving the prioritization of children at the emergency department: Updating the manchester triage system using...

    2021

  80. [88]

    Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies

    Susannah Fleming, Matthew Thompson, Richard Stevens, Carl Heneghan, Annette Plüddemann, Ian Maconochie, Lionel Tarassenko, and David Mant. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. ...

    2011

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.