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REVIEW 4 major objections 4 minor 113 references

Mammal: Supporting Breastfeeding Monitoring Through Computational Garments with Inter-Body Sensing

T0 review · 4 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read A caregiver-worn nursing garment can monitor an infant's latch, heart rate, feeding rhythm, and milk intake during breastfeeding without any sensors attached to the baby.

desk verdict Mammal is a clever feasibility study with a strong core idea, but the milk-intake result as written is contradicted by its own equations and event counts; the intake claim needs correction or removal. read the letter →

arxiv 2607.19796 v1 pith:7ZP6U43O submitted 2026-07-22 cs.HC cs.AR

Yanfeng Zhao , Morgan Geck , Kate Fernandez , Madison Nicole Jones , Xia Zhou , Jessica L. Ridgway , Te-Yen Wu This is my paper · ORCID
classification cs.HCcs.AR
keywords breastfeedingmonitoringinter-bodysensingwearableECGcomputationalgarmentinfantheartratesuck-swallow-breatheratiomilkintakeestimationlatchdetection
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

This paper tries to show that a caregiver-worn nursing garment can monitor the key quantities of a breastfeeding session—when the baby latched and for how long, the baby's heart rate during the feed, the suck–swallow–breathe ratio, and how much milk was transferred—without placing any sensor on the infant. The trick is that the baby's mouth on the breast creates a natural electrical and acoustic bridge: the infant's heart signal and feeding sounds travel through the contact and are picked up by electrodes and a contact microphone sewn into the caregiver's clothing. In a 10-dyad study the reported errors are 5.56% for latch duration, 3.61 bpm for heart rate, 0.12 for SSB ratio, and 15.76% for milk intake. If the method holds up, routine at-home breastfeeding monitoring could become a passive by-product of wearing a smart nursing top, opening a window on infant feeding competence and cardiovascular health. The main thing that has to be true for any of this to work is that the baby's bare skin stays in contact with a waist electrode while feeding.

What carries the argument

Inter-body signal transmission across the mouth-to-breast contact is the load-bearing mechanism. The latch forms two coupled pathways: a low-impedance electrical pathway (oral moisture and soft tissue conduct the infant's ECG onto the caregiver's torso) and a mechanical/acoustic pathway (sucking and swallowing vibrations propagate through the nipple and breast tissue). The garment's textile electrodes on the waist and upper back capture the mixed ECG, and a contact microphone under the breast captures the acoustics. Latch is detected from the R-amplitude ratio between caregiver ECG and inter-body ECG (threshold 0.8); infant ECG is separated from the caregiver's by a wavelet-subband adaptive

What would settle it

A controlled session in which the infant is fully clothed except for mouth contact, with no bare skin touching the waist electrode, should yield no separable infant ECG; if a usable ECG still appears, the electrical pathway model is wrong. Conversely, in a home-style feed with a swaddled infant, most sessions should fail—if they do not, the stated contact requirement is not actually limiting.

Watch

Extended reading notes

Core claim

On the paper's own terms, Mammal establishes that the mouth-to-breast contact during breastfeeding is not just a feeding interface but a usable sensing channel. The oral seal has measurably lower electrical impedance than ordinary skin-to-skin contact (R-amplitude ratio 0.91±0.03 vs 0.64±0.07 in an adult surrogate experiment), so latch onset can be read from the coupling strength; infant ECG rides on the caregiver's body and can be separated from the caregiver's ECG; sucking and swallowing vibrations survive transmission to the breast and can be detected. Feeding these signal streams together yields the four target metrics at the reported accuracies in a 10-dyad study, and a preliminary NICU

Load-bearing premise

For the ECG channel to work, the infant's bare skin—typically a hand or foot—must rest against the garment's waist electrode while the mouth is on the breast; if the baby is swaddled, clothed, or positioned so no bare skin touches that electrode, the infant ECG, heart rate, breathing, and SSB estimates all disappear.

Editorial extensions

If this is right

  • Latch onset and duration are recoverable from coupling strength alone: 102 latch onsets were detected with F1 0.93 and duration estimated within 5.56% MAPE, even in sessions with incidental skin-to-skin contact.
  • Infant heart rate during feeding is inferred from the separated inter-body ECG with 3.61 bpm MAE, and the paper reports that within-session HR and HRV trajectories track PPG-derived ground truth (r ≈ 0.95–0.96).
  • Suck and swallow events are detectable from the caregiver's body with F1 0.92–0.93, making the suck–swallow–breathe ratio measurable (0.12 MAE) as a marker of feeding coordination.
  • Milk intake is estimated from detected sucks and inter-suck intervals with 15.76% mean relative error, and an age-calibrated version of the model lowers the error to 2.91–4.76% in this small cohort.
  • A preliminary NICU clinician review suggests the resulting summaries could support monitoring during the NICU-to-home transition, a scenario the paper identifies as a likely first use.

Reading between the lines

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

  • A home deployment would likely expose much lower usable-data coverage than the lab study: because the ECG pathway requires the infant's bare skin on the waist electrode, feeds with swaddled or clothed infants would silently drop the heart-rate, breathing, and SSB outputs—the paper lists this as its key limitation, and quantifying the real-world dropout rate is the natural next experiment.
  • The milk-intake estimator inherits a 1982 relationship between inter-suck interval and milk transfer; the paper's own age-split results suggest the mapping shifts with infant age, so accuracy for newborns (under 2 months) is untested and may need a developmental recalibration rather than a single global curve.
  • If the inter-body channel is as reliable as reported, the same garment-based principle could extend to other contact-rich caregiving contexts—skin-to-skin holding, bottle feeding, or post-feed settling—where the electrode and acoustic placements would need to be re-derived for each contact site; Mammal does not claim these extensions.
  • A concrete improvement to test: replacing the exposed waist electrode with an actively shielded capacitive electrode could remove the bare-skin-contact requirement entirely (the paper names this as future work); verifying that in a swaddle condition would directly address the weakest assumption.
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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper presents Mammal, a caregiver-worn nursing tank top that uses inter-body electrical and acoustic sensing to monitor breastfeeding. The system detects latch onset/duration, separates infant ECG from the mixed caregiver-infant signal, detects suck and swallow events from contact-microphone signals, and estimates infant heart rate, suck-swallow-breathe (SSB) ratio, and milk intake. In a study with 10 caregiver-infant dyads, the authors report a MAPE of 5.56% for latch duration, an MAE of 3.61 bpm for infant heart rate, an MAE of 0.12 for SSB ratio, and a mean relative error of 15.76% for milk intake, together with high comfort and wearability ratings. The central claim is that a caregiver-worn garment can passively derive these metrics without instrumenting the infant.

Significance. If the reported accuracy holds, Mammal would be a meaningful contribution to at-home breastfeeding monitoring, addressing a gap between clinical observation and burden-free sensing. The inter-body ECG and acoustic characterization experiments are thoughtful, and the per-session result tables and micro-benchmarks provide useful detail. However, the milk-intake result is internally inconsistent with the paper's own data and equations, and the age-calibrated model is fit on the evaluation cohort. These issues currently prevent verification of the headline claims, so the contribution cannot be assessed until they are resolved.

major comments (4)
  1. [§6.4, §8.4 (Eqs. 8–9; Tables 5 and 7)] The reported 15.76% milk-intake error cannot be reproduced from the paper's own data. Table 7 lists 696–1,576 detected sucks per session and Table 5 lists latch durations of 15.9–32.5 min, implying mean inter-suck intervals of roughly 0.99–2.85 s. Applying Eq. (8) with a=578, b=776, cap=1300 ms gives m≈0.54–0.93 g per suck; summing over detected sucks yields 400–1,200 g per session, an order of magnitude above the 61–125 g test-weighing references in §8.4. The authors must clarify whether the 'sucking events' used for intake are the same quantity as in Table 7, how ISI is computed (across all successive sucks or within bursts), and whether the constants/units in Eq. (8) are correct. Reporting per-session estimated versus actual intake and releasing code/data would resolve this.
  2. [§8.4 (age-calibrated intake model)] The age-calibrated intake model refits a, b, and cap on the same study cohort, split into N=4 (<6 months) and N=5 (≥6 months), and evaluates via leave-one-out. This is a post-hoc model search over the study data; the reported 4.76% and 2.91% MAPE values are therefore fitted results, not independent predictions of age dependence. Combined with the inconsistency in the preceding comment, these numbers cannot be interpreted as validation. Please present the fitted parameters, per-fold predictions, and either an independent test set or a clear statement that these are exploratory model-fitting results.
  3. [§5.1, §6.1, §9.2] The ECG channel depends on direct skin contact between the infant's bare hand or foot and the waist electrode. The paper acknowledges this in §9.2, but the evaluation does not report how often and for how long that contact occurred across the 10 sessions. The reported HR and SSB metrics are thus conditional on contact being maintained. Without quantifying contact availability and performance conditioned on contact versus no-contact periods, the 'passive monitoring' claim is stronger than the evidence supports. Please provide per-session contact duration and a breakdown of performance when contact is lost.
  4. [§8.3] Ground-truth swallowing events were annotated by a machine-learning model (YAMNet) applied to external audio, not by direct human observation, with only low-confidence events reviewed by a medical student. The reported swallowing F1=0.93 is therefore agreement with an algorithmic proxy rather than with human ground truth. The authors should validate the proxy against human annotation on at least a subset of data, or discuss how label noise affects the reported F1 and the downstream SSB ratio.
minor comments (4)
  1. [Eq. (5)] The heart-rate formula is written H = 60 / (1/10 * sum d_i). This is mathematically equivalent to 600/sum(d_i), but the notation is easy to misread; please add parentheses and define d_i explicitly as seconds per RR interval.
  2. [Tables 6 and 8.4 text] Table 6 reports SSB MAE as 'per session' in the header, while §8.4 states the MAE is per 10-second window. This is inconsistent; please align the units.
  3. [§6.2] Duplicate word in the sentence 'the the theoretical caregiver ECG component' — please fix.
  4. [Abstract and §8.4] The abstract says 'mean relative error of 15.76%' for milk intake, while §8.4 also uses 'mean relative error' but later introduces 'MAPE' for the age-calibrated model. Please use consistent terminology (e.g., MRE vs. MAPE) throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: the four headline metrics are benchmarked against independent external ground truth, and the age-calibrated intake model is a transparent leave-one-out calibration rather than a hidden fit.

full rationale

Each headline sensing target is evaluated against independently collected ground truth rather than derived from Mammal's own outputs: latch duration against caregiver button annotations and external audio, infant heart rate against ankle-worn Bangle.js PPG (itself calibrated against an FDA pulse oximeter), SSB ratio against externally monitored swallowing audio plus PPG-derived breaths, and milk intake against pre/post-feed test-weighing. The ECG separation borrows a signal model from prior work [79], but the paper independently characterizes mouth-to-skin electrical and acoustic transmission in Experiments 1 and 2 and validates the downstream HR on the 10-dyad study, so the self-citation is not load-bearing. The milk-intake model (Eq. 8) is imported from an external 1982 study rather than fit to the present cohort; the subsequent age-calibrated model is explicitly a leave-one-out cross-validation on the same sessions, and the paper labels it preliminary and notes the small subgroup sizes. A reader cannot reproduce the reported 15.76% milk-intake MRE from the stated Eq. 8 constants and Table 7 suck counts—the arithmetic suggests a roughly 10x overestimate—which is a serious reproducibility/correctness concern, but it is an internal inconsistency rather than a prediction that reduces by construction to a fitted input. The paper's own Sections 9.2 and 9.3 disclose the contact-electrode failure mode and the small-sample feasibility status. No step in the derivation chain equates an output to an input by definition, so there is no significant circularity.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central sensing chain rests on six fitted or empirically chosen parameters and six domain assumptions. The age-calibrated milk-intake model is the clearest case of fitting to the evaluation cohort. No new physical entities, particles, or forces are postulated.

free parameters (6)
  • Latch detection R-amplitude ratio threshold = 0.8
    Empirically determined threshold for declaring mouth-to-breast contact (§6.4).
  • Morphological closing structuring element = 0.8 s
    Used to smooth the latch-state signal; 'determined from pilot study [79]' (§6.4).
  • Fuzzy clustering membership threshold = 0.85
    Classifies a window as an event when max membership exceeds 0.85; selected empirically in pilot study (§6.3).
  • Number of temporal segments in ECG separation = n = 2
    Chosen empirically; ablation shows two 0.5 s segments outperform three 0.33 s segments (§6.2, §8.5).
  • DWT decomposition level for ECG separation = Level 4
    Selected via ablation; Level 4 gives best correlation/MAE trade-off (§8.5).
  • Age-calibrated milk-intake model parameters a, b, cap = a, b, cap for <6mo and >=6mo; values not reported
    Refit to the same study cohort split by age in leave-one-out evaluation; the 4.76%/2.91% MAPE is a fitted result (§8.4, Eq. 8).
assumptions (6)
  • domain assumption Mouth-to-skin contact has lower electrical impedance than general skin-to-skin contact, making latch detectable via coupling strength.
    Experiment 1 uses adult lip-to-hand contact as a proxy for mouth-to-breast contact; the assumption is plausible but not directly validated on real dyads (§5.1).
  • domain assumption A silicone artificial breast adequately represents human breast tissue for acoustic propagation characterization.
    The paper states the silicone model is homogeneous and cannot fully capture acoustic damping, but uses it as a benchtop testbed (§5.2).
  • domain assumption PPG-derived heart rate and respiration from an ankle smartwatch are a valid ground truth for infants.
    The Bangle.js validation was conducted only with five adults, not infants; the paper relies on literature that PPG tracks ECG in healthy adults (§8.3).
  • domain assumption The 1982 inter-suck-interval-to-milk-transfer model (Eq. 8) transfers to caregiver-worn detected sucks without in-situ calibration.
    Used directly for the headline 15.76% intake error; the age-calibrated refit suggests the mapping is population-dependent (§6.4, §8.4).
  • domain assumption The inter-body ECG signal model from Joey [79] applies to breastfeeding with time-varying coupling.
    Equations (1)-(3) are imported from prior work and adapted with time-local weights; no first-principles derivation is given here (§6.2).
  • domain assumption The pretrained DeScoD-ECG denoiser generalizes to separated infant ECG morphology.
    Used as a black-box denoiser triggered by an energy heuristic; no retraining or infant-specific validation is reported (§6.2, §8.5).

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Cite this review

Pith. "Pith review of Mammal: Supporting Breastfeeding Monitoring Through Computational Garments with Inter-Body Sensing." pith.science (2026). https://pith.science/paper/7ZP6U43O

@misc{pith2026260719796,
  author       = {Pith},
  title        = {Pith review of: Mammal: Supporting Breastfeeding Monitoring Through Computational Garments with Inter-Body Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7ZP6U43O}},
  note         = {Machine review of arXiv:2607.19796}
}
read the original abstract

Breastfeeding provides critical insight into infant feeding competence and physiological health, yet objective monitoring remains difficult due to the intimate and internal nature of feeding. We present Mammal, a caregiver-worn computational garment that unobtrusively monitors breastfeeding without attaching sensors to the infant. Mammal leverages inter-body signal transmission through natural mouth-to-breast contact to capture infant cardiac and feeding-related acoustic signals on the caregiver's body. Using novel algorithms to detect latch onset, infer infant electrocardiogram (ECG), and identify suck and swallow events from inter-body signals, Mammal estimates latch duration, in-feeding heart rate, suck-swallow-breathe (SSB) ratio, and milk intake. In a user study with 10 caregiver-infant dyads, Mammal achieves a mean absolute percentage error (MAPE) of 5.56% for latch duration, a mean absolute error (MAE) of 3.61 bpm for infant heart rate estimation, a mean absolute error of 0.12 for SSB ratio estimation, and a mean relative error of 15.76% for milk intake, with participants reporting high comfort and wearability.

Figures

Figures reproduced from arXiv: 2607.19796 by the authors.

Figure 1
Figure 1. We introduce Mammal, a caregiver-worn computational garment that monitors daily breastfeeding using inter-body [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (a) Conceptual illustration of inter-body signal transmission during breastfeeding, showing how infant ECG signals [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Study setup and exemplary ECG signals recorded in Experiment 1 under three contact conditions: (a) general skin-to [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: (a) Experiment 2 setup. (b) Average SNR of sucking and swallowing sounds via inter-body transmission. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Garment design and sensor placement. (a) Five common breastfeeding postures, highlighting infant contact near the [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Overview of the ECG separation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Overview of the algorithm for sucking and swallowing detection. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Latch monitoring performance. (a) Confusion matrix for latch onset detection aggregated across the 10 sessions. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: (a) Bland-Altman plot of Mammal-derived HR and the ground-truth HR from PPG. (b) Mammal-derived HR and HRV [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: (a) Confusion matrix for sucking detection. (b) Confusion matrix for swallowing detection. (c) Average F1 score of [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: Mammal-estimated SSB ratio aligns with the annotated ground-truth SSB ratio over time. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: Estimated versus ground-truth breastmilk intake across sessions using (a) the original ISI-based milk-intake model [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 13
Figure 13. Figure 13: Clinical-grade ECG validation across three adult participants. (a) The ECG waveform highlights the QRS complex [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Mean caregiver ratings of the Mammal garment on comfort, wearability, and overall satisfaction. [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]
Figure 15
Figure 15. Figure 15: Study environment used in the breastfeeding sessions. The room included a breastfeeding couch and a long chair to [PITH_FULL_IMAGE:figures/full_fig_p034_15.png]
Figure 16
Figure 16. Figure 16: Representative example of ECG separation during temporally overlapping heartbeats in the adult-pair pilot study. [PITH_FULL_IMAGE:figures/full_fig_p036_16.png]
Figure 17
Figure 17. Figure 17: Example of conditional DeScoD-ECG denoising. From top to bottom, the plots show the mixed inter-body ECG, the [PITH_FULL_IMAGE:figures/full_fig_p036_17.png]

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Works this paper leans on

113 extracted references · 57 canonical work pages

  1. [1]

    Adafruit Industries. 2021. Knit Conductive Fabric - Silver 20cm square. Adafruit Product ID: 1167. https://www.adafruit.com/product/ 1167 Accessed: 2026-01-14

    2021

  2. [2]

    Adafruit Industries. 2023. Stainless Thin Conductive Yarn / Thick Conductive Thread - 30 ft. Adafruit Product ID: 603. https: //www.adafruit.com/product/603 Accessed: 2026-01-29

    2023

  3. [3]

    Bella Eszter Ajtay, Szabolcs Béres, and László Hejjel. 2023. The oscillating pulse arrival time as a physiological explanation regarding the difference between ECG- and Photoplethysmogram-derived heart rate variability parameters.Biomedical Signal Processing and Control79 (2023), 104033. doi:10.1016/j.bspc.2022.104033

    arXiv 2023

  4. [4]

    Oliver Amft, Mathias Stäger, Paul Lukowicz, and Gerhard Tröster. 2005. Analysis of chewing sounds for dietary monitoring. In Proceedings of the 7th International Conference on Ubiquitous Computing(Tokyo, Japan)(UbiComp’05). Springer-Verlag, Berlin, Heidelberg, 56–72. doi:10.1007/11551201_4

    work page doi:10.1007/11551201_4 2005

  5. [5]

    Association for the Advancement of Medical Instrumentation. 2002. Cardiac monitors, heart rate meters, and alarms (ANSI/AAMI EC13:2002). https://mdcpp.com/doc/standard/ANSIAAMIEC13-2002.pdf Accessed: 2026-01-30

    2002

  6. [6]

    Bangle.js Project. 2019. Bangle.js: The World’s first Open Source Hackable Smart Watch. https://banglejs.com/ Accessed: 2026-02-01

    2019

  7. [7]

    Ben-Tal, S

    A. Ben-Tal, S. S. Shamailov, and J. F. Paton. 2012. Evaluating the physiological significance of respiratory sinus arrhythmia: looking beyond ventilation-perfusion efficiency.The Journal of Physiology590, 8 (apr 2012), 1989–2008. doi:10.1113/jphysiol.2011.222422

    arXiv 2012

  8. [8]

    MW Borschel, A Kirksey, and RE Hannemann. 1986. Evaluation of test-weighing for the assessment of milk volume intake of formula-fed infants and its application to breast-fed infants.The American Journal of Clinical Nutrition43, 3 (1986), 367–373. doi:10.1093/ajcn/43.3.367

    work page doi:10.1093/ajcn/43.3.367 1986

Show all 113 references
  1. [9]

    Bowen-Jones, C

    A. Bowen-Jones, C. Thompson, and R. F. Drewett. 1982. Milk Flow and Sucking Rates during Breast-feeding.Developmental Medicine & Child Neurology24, 6 (1982), 626–633. doi:10.1111/j.1469-8749.1982.tb13673.x

    1982

  2. [10]

    Bu’Lock, M

    F. Bu’Lock, M. W. Woolridge, and J. D. Baum. 1990. Development Of Co-Ordination Of Sucking, Swallowing And Breathing: Ultrasound Study Of Term And Preterm Infants.Developmental Medicine & Child Neurology32, 8 (1990), 669–678. doi:10.1111/j.1469-8749.1990. tb08427.x

    1990

  3. [11]

    Karin Cadwell. 2007. Latching-on and suckling of the healthy term neonate: Breastfeeding assessment.Journal of midwifery & women’s health52, 6 (2007), 638–642. doi:10.1016/j.jmwh.2007.08.004

    2007 doi

  4. [12]

    Belinda Chan, Anne Woodbury, Libbi Hazelwood, and Yogen Singh. 2025. Feeding Approach to Optimizing Nutrition in Infants with Congenital Heart Disease.Journal of Cardiovascular Development and Disease12, 2 (2025). doi:10.3390/jcdd12020038

    2025 doi

  5. [13]

    Lucas, and Bin Feng

    Longtu Chen, Ruth F. Lucas, and Bin Feng. 2018. A Novel System to Measure Infants’ Nutritive Sucking During Breastfeeding: the Breastfeeding Diagnostic Device (BDD).IEEE Journal of Translational Engineering in Health and Medicine6 (2018), 2700208. doi:10.1109/JTEHM.2018.2838139

    2018

  6. [14]

    Siluo Chen, Yu Chen, Jing Huang, Chengyu Liu, and Kan Luo. 2025. Open-source low-cost non-contact ECG monitoring system using active dry electrodes.HardwareX24 (2025), e00718. doi:10.1016/j.ohx.2025.e00718

    2025 doi

  7. [15]

    Ha Uk Chung, Alina Y Rwei, Aurélie Hourlier-Fargette, et al. 2020. Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units.Nature Medicine26, 3 (2020), 418–429. doi:10.1038/s41591-020-0792-9

    2020 doi

  8. [16]

    da Costa, L

    S. da Costa, L. van den Engel-hoek, and A. Bos. 2008. Sucking and swallowing in infants and diagnostic tools.Journal of Perinatology 28, 4 (apr 2008), 247–257. doi:10.1038/sj.jp.7211924

    2008 doi

  9. [17]

    Nadine Dabby, Aleksandar Aleksov, Eric Lewallen, Sasha Oster, Racquel Fygenson, Braxton Lathrop, Michael Bynum, Mezhgan Samady, Steven Klein, and Steven Girouard. 2017. A scalable process for manufacturing integrated, washable smart garments applied to heart rate monitoring(IS...

    2017

  10. [18]

    Mark Ansermino, and Guy A

    Parastoo Dehkordi, Ainara Garde, Behnam Molavi, J. Mark Ansermino, and Guy A. Dumont. 2018. Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations.Frontiers in Physiology9 (2018). doi:10.3389/fphys.2018.00948 Proc. ACM Interac...

    2018

  11. [19]

    DigiKey Electronics. 2026. PSR1636-10: 10 Conductor 0.050" (1.27 mm) Flat Flex Cable. https://www.digikey.com/en/products/detail/ parlex-usa-llc/PSR1636-10/469763 Accessed: 2026-02-01

    2026

  12. [20]

    Digilent. 2023. Analog Discovery 3: 125 MS/s USB Oscilloscope, Waveform Generator, Logic Analyzer, and Variable Power Supply. https://digilent.com/shop/products/mixed-signal-oscilloscopes/discovery-essentials/analog-discovery-3/ Accessed: 2026-01-29

    2023

  13. [21]

    Dogan and R

    H. Dogan and R. O. Dogan. 2023. A Comprehensive Review of Computer-based Techniques for R-Peaks/QRS Complex Detection in ECG Signal.Archives of Computational Methods in Engineering30 (2023), 3703–3721. doi:10.1007/s11831-023-09916-x

    2023 doi

  14. [22]

    Pamela Douglas. 2022. Re-thinking lactation-related nipple pain and damage.Women’s Health18 (2022), 17455057221087865. doi:10.1177/17455057221087865

    2022 doi

  15. [23]

    Dudik, Iva Jestrović, Bo Luan, James L

    Joshua M. Dudik, Iva Jestrović, Bo Luan, James L. Coyle, and Ervin Sejdić. 2015. A comparative analysis of swallowing accelerometry and sounds during saliva swallows.Biomedical Engineering Online14 (2015), 3. doi:10.1186/1475-925X-14-3

    2015 doi

  16. [24]

    Fukuike, Naoki Kodama, Y

    C. Fukuike, Naoki Kodama, Y. Manda, Y. Hashimoto, K. Sugimoto, A. Hirata, Q. Pan, N. Maeda, and Shogo Minagi. 2014. A novel automated detection system for swallowing sounds during eating and speech under everyday conditions.Journal of Oral Rehabilitation 42 (dec 2014). doi:10....

    2014 doi

  17. [25]

    Hall, Matthew Walker, Chloe Tattersfield, Stephanie Post, Jessica Fitzjohn, Cong Zhou, and J.Geoffery Chase

    Francesca Gallaway, Henry W. Hall, Matthew Walker, Chloe Tattersfield, Stephanie Post, Jessica Fitzjohn, Cong Zhou, and J.Geoffery Chase. 2025. Silicone Breast Phantoms for use with Digital Imaging Elasto-Tomography Breast Cancer Screening.HardwareX24 (2025), e00707. doi:10.10...

    2025 doi

  18. [26]

    Lisa Gatti. 2008. Maternal Perceptions of Insufficient Milk Supply in Breastfeeding.Journal of Nursing Scholarship40, 4 (2008), 355–363. doi:10.1111/j.1547-5069.2008.00234.x

    2008

  19. [27]

    Ira H Gewolb, Frank L Vice, Erika L Schweitzer-Kenney, Vicki L Taciak, and James F Bosma. 2001. Developmental patterns of rhythmic suck and swallow in preterm infants.Developmental Medicine & Child Neurology43, 1 (2001), 22–27. doi:10.1017/s0012162201000044

    2001 doi

  20. [28]

    Goldfield, Michael J

    Eugene C. Goldfield, Michael J. Richardson, K. G. Lee, et al. 2006. Coordination of Sucking, Swallowing, and Breathing and Oxygen Saturation During Early Infant Breast-feeding and Bottle-feeding.Pediatric Research60, 4 (2006), 450–455. doi:10.1203/01.pdr.0000238378. 24238.9d

    2006 doi

  21. [29]

    Sharp, Christos Papavassiliou, Mary J

    Valentin Goverdovsky, Wilhelm von Rosenberg, Takashi Nakamura, David Looney, David J. Sharp, Christos Papavassiliou, Mary J. Morrell, and Danilo P. Mandic. 2017. Hearables: Multimodal physiological in-ear sensing.Scientific Reports7, 1 (July 2017), 6948. doi:10.1038/s41598-017-06925-2

    2017 doi

  22. [30]

    Chris Harrison, Desney Tan, and Dan Morris. 2010. Skinput: appropriating the body as an input surface. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Atlanta, Georgia, USA)(CHI ’10). Association for Computing Machinery, New York, NY, USA, 453–462....

    2010

  23. [31]

    Horta and Cesar G

    Bernardo L. Horta and Cesar G. Victora. 2013.Long-term effects of breastfeeding: A systematic review. Technical Report ISBN 9789241505307. World Health Organization. https://www.who.int/publications/i/item/9789241505307

    2013

  24. [32]

    Hoyt-Austin, Laura R

    Adrienne E. Hoyt-Austin, Laura R. Kair, Ilse A. Larson, Elizabeth K. Stehel, and the Academy of Breastfeeding Medicine. 2022. Academy of Breastfeeding Medicine Clinical Protocol #2: Guidelines for Birth Hospitalization Discharge of Breastfeeding Dyads, Revised 2022. Breastfeed...

    2022

  25. [33]

    J. Kim, S. Oh, R. Avila, et al. 2025. A compact, wireless system for continuous monitoring of breast milk expressed during breastfeeding. Nature Biomedical Engineering9 (2025), 1645–1655. doi:10.1038/s41551-025-01393-w

    2025 doi

  26. [34]

    Michael S Kramer and Ritsuko Kakuma. 2012. Optimal duration of exclusive breastfeeding.Cochrane database of systematic reviews8 (2012). doi:10.1002/14651858.CD003517.pub2

    2012 doi

  27. [35]

    Cowie, Zixuan Ding, Andrew Dymond, Stephan M

    Florian Kristof, Maximilian Kapsecker, Leon Nissen, James Brimicombe, Martin R. Cowie, Zixuan Ding, Andrew Dymond, Stephan M. Jonas, Hannah Clair Lindén, Gregory Y. H. Lip, Kate Williams, Jonathan Mant, Peter H. Charlton, and on behalf of the SAFER Investigators

  28. [36]

    Hanne Kronborg and Michael Vaeth. 2009. How are effective breastfeeding technique and pacifier use related to breastfeeding problems and breastfeeding duration?Birth36, 1 (2009), 34–42. doi:10.1111/j.1523-536X.2008.00293.x

    2009 arXiv

  29. [37]

    Gierad Laput, Robert Xiao, and Chris Harrison. 2016. ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers. InProceedings of the 29th Annual Symposium on User Interface Software and Technology(Tokyo, Japan)(UIST ’16). Association for Computing Ma...

    2016

  30. [39]

    Chantal Lau. 2015. Development of Suck and Swallow Mechanisms in Infants.Annals of Nutrition and Metabolism66, Suppl. 5 (07 2015), 7–14. doi:10.1159/000381361

    2015 doi

  31. [40]

    C. Lau, E. O. Smith, and R. J. Schanler. 2003. Coordination of suck-swallow and swallow respiration in preterm infants.Acta Paediatrica 92, 6 (2003), 721–727. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 10, No. 3, Article 194. Publication date: September 2026. ...

    2003

  32. [41]

    Jerome R. Lechien. 2024. Management of Pediatric Bilateral Vocal Fold Paralysis: A State-of-the-Art Review of Etiologies, Diagnosis, and Treatments.Children11, 4 (2024), 398. doi:10.3390/children11040398

    2024 doi

  33. [42]

    Huayu Li, Gregory Ditzler, Janet Roveda, and Ao Li. 2024. DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal.IEEE Journal of Biomedical and Health Informatics28, 9 (2024), 5081–5091. doi:10.1109/JBHI.2023.3237712

    2024

  34. [43]

    Maoyuan Li, Yong Song, Wansong Li, Guangfa Wang, Tianpeng Bu, Yufei Zhao, and Qun Hao. 2017. The Modeling and Simulation of the Galvanic Coupling Intra-Body Communication via Handshake Channel.Sensors17, 4 (April 2017), 863. doi:10.3390/s17040863

    2017 doi

  35. [44]

    Wen-Jie Li, Yue-Can Gao, Xiao Hu, Yu-Tong Tan, Jia-Jun Deng, Hai-Feng Pan, and Sha-Sha Tao. 2025. Association between breastfeeding and the risk of autoimmune diseases: A systematic review and meta-analysis.Autoimmunity Reviews24, 6 (2025), 103801. doi:10.1016/j. autrev.2025.103801

    2025

  36. [45]

    Chen Liu, Jie Xiong, Lin Cai, Lin Feng, Xiaojiang Chen, and Dingyi Fang. 2019. Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies3, 3, Article 96 (Sept. 2019), 22...

    2019 doi

  37. [46]

    Shovan Maity, Debayan Das, Baibhab Chatterjee, and Shreyas Sen. 2018. Characterization and Classification of Human Body Channel as a function of Excitation and Termination Modalities. In2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology S...

    2018

  38. [47]

    Shovan Maity, David Yang, Scott Stanton Redford, Debayan Das, Baibhab Chatterjee, and Shreyas Sen. 2020. BodyWire-HCI: Enabling New Interaction Modalities by Communicating Strictly During Touch Using Electro-Quasistatic Human Body Communication.ACM Trans. Comput.-Hum. Interact...

    2020 doi

  39. [48]

    must have

    A. Malik and Z. Ehsan. 2020. Media Review: The Owlet Smart Sock—a “must have” for the baby registry?Journal of Clinical Sleep Medicine16, 5 (may 2020), 839–840. doi:10.5664/jcsm.8400

    2020 doi

  40. [49]

    N. R. A. Martins, F. Bauer, F. Baty, et al. 2025. Introduction to electrocardiogram signal quality assessment and estimated accuracy for textile electrodes.Scientific Reports15 (2025), 41365. doi:10.1038/s41598-025-25365-x

    2025 doi

  41. [50]

    Masimo. 2019. MightySat™Rx Fingertip Pulse Oximeter. https://techdocs.masimo.com/globalassets/techdocs/pdf/lab-10169a_master. pdf Accessed: 2026-01-29

    2019

  42. [51]

    Irving, Alexandra L

    Barbara Medoff-Cooper, Sharon Y. Irving, Alexandra L. Hanlon, Nadya Golfenshtein, Jerilynn Radcliffe, Virginia A. Stallings, Bradley S. Marino, and Chitra Ravishankar. 2016. The Association among Feeding Mode, Growth, and Developmental Outcomes in Infants with Complex Congenit...

    2016 doi

  43. [52]

    Claire K Miller. 2011. Feeding issues and interventions in infants and children with clefts and craniofacial syndromes. InSeminars in speech and language, Vol. 32. 115–126. doi:10.1055/s-0031-1277714

    2011 doi

  44. [53]

    Miller and Pakaphan Kiatchoosakun

    Martha J. Miller and Pakaphan Kiatchoosakun. 2004. Relationship between respiratory control and feeding in the developing infant. Seminars in Neonatology9, 3 (2004), 221–227. doi:10.1016/j.siny.2003.11.006

    2004 doi

  45. [54]

    Varun Mishra, Sougata Sen, Grace Chen, Tian Hao, Jeffrey Rogers, Ching-Hua Chen, and David Kotz. 2020. Evaluating the Reproducibility of Physiological Stress Detection Models.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.4, 4, Article 147 (Dec. 2020), 29 pages. doi:10.1...

    2020 doi

  46. [55]

    Katsumi Mizuno and Aki Ueda. 2003. The maturation and coordination of sucking, swallowing, and respiration in preterm infants.The Journal of Pediatrics142, 1 (2003), 36–40. doi:10.1067/mpd.2003.mpd0312

    2003 doi

  47. [56]

    Julio César Muro-Valdez, Alejandra Meza-Rios, Blanca Rosa Aguilar-Uscanga, Rocio Ivette Lopez-Roa, Eunice Medina-Díaz, Esmer- alda Marisol Franco-Torres, and Adelaida Sara Minia Zepeda-Morales. 2023. Breastfeeding-Related Health Benefits in Children and Mothers: Vital Organs P...

    2023 doi

  48. [57]

    Muth and Mary Tanaka

    Natalie D. Muth and Mary Tanaka. 2023. Nutrition in Infancy. InThe Clinician’s Guide to Pediatric Nutrition. American Academy of Pediatrics

    2023

  49. [58]

    Haruka Nakagawa, Sachine Yoshida, Ayumi Ohnishi, Tsutomu Terada, Hiromasa Funato, and Masahiko Tsukamoto. 2019. Effect of using smartphone during breast-feeding(UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 1190–1193. doi:10.1145/3341162.3344840

    2019

  50. [59]

    Marianne R Neifert. 2004. Breastmilk transfer: positioning, latch-on, and screening for problems in milk transfer.Clinical obstetrics and gynecology47, 3 (2004), 656–675. doi:10.1097/01.grf.0000136183.12304.96

    2004

  51. [60]

    Abreha Bayrau Nigusse, Benny Malengier, Desalegn Alemu Mengistie, Granch Berhe Tseghai, and Lieva Van Langenhove. 2020. Development of Washable Silver Printed Textile Electrodes for Long-Term ECG Monitoring.Sensors20, 21 (2020). doi:10.3390/s20216233

    2020 doi

  52. [61]

    OpenBCI. 2025. Cyton Getting Started Guide. https://docs.openbci.com/GettingStarted/Boards/CytonGS/. Accessed: 2026-04-26

    2025

  53. [62]

    OpenBCI. 2026. Cyton Biosensing Board. OpenBCI Product: Cyton Biosensing Board 8 channel. https://shop.openbci.com/products/ cyton-biosensing-board-8-channel Accessed: 2026-01-27

    2026

  54. [63]

    OpenBCI. 2026. Kendall EMG/ECG Foam Solid Gel Electrodes. https://shop.openbci.com/products/kendall-emg-ecg-foam-solid-gel- electrodes-50-pack Accessed: 2026-01-14. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 10, No. 3, Article 194. Publication date: September ...

    2026

  55. [64]

    OpenBCI Documentation. 2026. Using SD Card with Cyton and CytonDaisy Board. https://docs.openbci.com/Cyton/CytonSDCard/ Accessed: 2026-02-01

    2026

  56. [65]

    Tompkins

    Jiapu Pan and Willis J. Tompkins. 1985. A Real-Time QRS Detection Algorithm.IEEE Transactions on Biomedical EngineeringBME-32, 3 (1985), 230–236. doi:10.1109/TBME.1985.325532

    1985

  57. [66]

    L. F. Pava, P. Perafán, M. Badiel, J. J. Arango, L. Mont, C. A. Morillo, and J. Brugada. 2010. R-wave peak time at DII: a new criterion for differentiating between wide complex QRS tachycardias.Heart Rhythm7, 7 (jul 2010), 922–926. doi:10.1016/j.hrthm.2010.03.001

    2010 doi

  58. [67]

    Prime, Donna T

    Danielle K. Prime, Donna T. Geddes, Diane L. Spatz, et al. 2009. Using milk flow rate to investigate milk ejection in the left and right breasts during simultaneous breast expression in women.International Breastfeeding Journal4 (2009), 10. doi:10.1186/1746-4358-4-10

    2009 doi

  59. [68]

    Adams, Mi Zhang, Erin Cherry, Bobby Zhou, Huaishu Peng, and Tanzeem Choudhury

    Tauhidur Rahman, Alexander T. Adams, Mi Zhang, Erin Cherry, Bobby Zhou, Huaishu Peng, and Tanzeem Choudhury. 2014. BodyBeat: a mobile system for sensing non-speech body sounds. InProceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Servi...

    2014

  60. [69]

    Nicholas Ravanelli, KarLee Lefebvre, Amy Brough, Simon Paquette, and Wei Lin. 2025. Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults.Sensors25, 9 (2025), 2926. doi:10.3390/s25092926

    2025 doi

  61. [70]

    Roberts, Jr., Richard D

    Jesse D. Roberts, Jr., Richard D. Walton, Virginie Loyer, Olivier Bernus, and Kanchan Kulkarni. 2024. Open-source software for respiratory rate estimation using single-lead electrocardiograms.Scientific Reports14, 1 (jan 2024), 167. doi:10.1038/s41598-023-50470-0

    2024 doi

  62. [71]

    RØDE. 2026. NTG5 Location Recording Kit. https://rode.com/en-us/products/ntg5. Accessed: 2026-04-26

    2026

  63. [72]

    R. D. Ross. 2012. The Ross classification for heart failure in children after 25 years: a review and an age-stratified revision.Pediatric Cardiology33, 8 (2012), 1295–1300. doi:10.1007/s00246-012-0306-8

    2012 doi

  64. [73]

    M. Rupp. 1993. The behavior of LMS and NLMS algorithms in the presence of spherically invariant processes.IEEE Transactions on Signal Processing41, 3 (1993), 1149–1160. doi:10.1109/78.205720

    1993 doi

  65. [74]

    Chuisano, Kaoer Ma, Aria Grabowski, Kate P

    Anna Sadovnikova, Samantha A. Chuisano, Kaoer Ma, Aria Grabowski, Kate P. Stanley, Katrina B. Mitchell, Anne Eglash, Jeffrey S. Plott, Ruth E. Zielinski, and Olivia S. Anderson. 2020. Development and evaluation of a high-fidelity lactation simulation model for health professio...

    2020 doi

  66. [75]

    Sakalidis, Holly L

    Vanessa S. Sakalidis, Holly L. McClellan, Anna R. Hepworth, Jacqueline C. Kent, Ching Tat Lai, Peter E. Hartmann, and Donna T. Geddes. 2012. Oxygen Saturation and Suck-Swallow-Breathe Coordination of Term Infants during Breastfeeding and Feeding from a Teat Releasing Milk Only...

    2012 doi

  67. [76]

    Neil Savage. 2020. Sibel Health: designing vital-sign sensors for delicate skin.Nature(jun 2020). https://www.nature.com/articles/d41586- 020-01806-7

    2020

  68. [77]

    O Savenije and Petrus Brand. 2006. Accuracy and precision of test weighing to assess milk intake in newborn infants.Archives of disease in childhood. Fetal and neonatal edition91 (10 2006), F330–2. doi:10.1136/adc.2005.091876

    2006 arXiv

  69. [78]

    Scanlon, Maria P

    Kelley S. Scanlon, Maria P. Alexander, Mary K. Serdula, Margarett K. Davis, and Barbara A. Bowman. 2002. Assessment of Infant Feeding: the Validity of Measuring Milk Intake.Nutrition Reviews60, 8 (08 2002), 235–251. doi:10.1301/002966402320289368

    2002 doi

  70. [79]

    Qijia Shao, Jiting Liu, Emily Bejerano, Ho Man Colman, Jingping Nie, Xiaofan Jiang, and Xia Zhou. 2024. Joey: Supporting Kangaroo Mother Care with Computational Fabrics. InProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services(Mina...

    2024

  71. [80]

    Shenzhen Biocare Bio-Medical Equipment Co., Ltd. 2015. iE 300 Digital 3-Channel ECG. https://biocare.com.cn/html/en/Products/ ECG/2015/1210/486.html Accessed: 2026-04-24

    2015

  72. [81]

    Simons, Laura L

    Jeffrey P. Simons, Laura L. Greenberg, Deepak K. Mehta, Anthony Fabio, Raymond C. Maguire, and David L. Mandell. 2016. Laryngo- malacia and swallowing function in children.The Laryngoscope126 (2016), 478–484. doi:10.1002/lary.25440

    2016 doi

  73. [82]

    S. So, T. Tadj, B. Schwerin, A. B. Chang, S. Humphries, and T. T. Frakking. 2026. Using Machine Learning for the Automated Segmentation and Detection of Swallows Obtained by Digital Cervical Auscultation in Preterm Neonates.Dysphagia41, 1 (Feb. 2026), 275–284. doi:10.1007/s004...

    2026 doi

  74. [83]

    Po-Cheng Su, Ya-Hsin Hsueh, Ming-Ta Ke, Jyun-Jhe Chen, and Ping-Chen Lai. 2021. Noncontact ECG Monitoring by Capacitive Coupling of Textiles in a Chair.Journal of Healthcare Engineering2021, 1 (2021), 6698567. doi:10.1155/2021/6698567

    2021 doi

  75. [84]

    Suiter and Jacki Ruark-McMurtrey

    Debra M. Suiter and Jacki Ruark-McMurtrey. 2007. Oxygen Saturation and Heart Rate During Feeding in Breast-Fed Infants at 1 Week and 2 Months of Age.Archives of Physical Medicine and Rehabilitation88, 12 (Dec. 2007), 1681–1685. doi:10.1016/j.apmr.2007.07.042

    2007 doi

  76. [85]

    B. A. Sullivan and K. D. Fairchild. 2022. Vital signs as physiomarkers of neonatal sepsis.Pediatric Research91, 2 (2022), 273–282. doi:10.1038/s41390-021-01709-x

    2022 doi

  77. [86]

    B. A. Sullivan, V. P. Nagraj, K. L. Berry, et al. 2021. Clinical and vital sign changes associated with late-onset sepsis in very low birth weight infants at 3 NICUs.Journal of Neonatal-Perinatal Medicine14, 4 (2021), 553–561. doi:10.3233/NPM-200578

    2021 doi

  78. [87]

    Deal, and Leonard S

    Borys Surawicz, Rory Childers, Barbara J. Deal, and Leonard S. Gettes. 2009. AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram.Circulation119, 10 (2009), e235–e240. doi:10.1161/CIRCULATIONAHA.108.191095 Proc. ACM Interact. Mob. We...

    2009 doi

  79. [88]

    Fabrizio Taffoni, Eleonora Tamilia, Micaela Giorgino, Giulia Morbidoni, Andrea Rosi, Alberto Scaini, Anna Maria Visco, Emiliano Schena, and Domenico Formica. 2016. A Novel System to Study the Coordination of Sucking and Breathing in Newborns During Bottle Feeding.IEEE Sensors ...

    2016

  80. [89]

    Kymeng Tang, Kathrin Gerling, Luc Geurts, and Katta Spiel. 2021. Understanding the Role of Technology to Support Breastfeeding. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’21). Association for Computing Machinery, New Y...

    2021

  81. [90]

    TE Connectivity. 2017. CONTACT MICROPHONE CM-01. TE Connectivity Product ID: 1007079-1. https://www.te.com/en/product- 1007079-1.html Accessed: 2026-01-27

    2017

  82. [91]

    TensorFlow. 2026. YAMNet. https://github.com/tensorflow/models/tree/master/research/audioset/yamnet. Accessed: 2026-4-26

    2026

  83. [92]

    Yayoi Tetsuou Tsukada, Miwa Tokita, Hiroshige Murata, Yasuhiro Hirasawa, Kenji Yodogawa, Yu-Ki Iwasaki, Kuniya Asai, Wataru Shimizu, Nahoko Kasai, Hiroshi Nakashima, and Shingo Tsukada. 2019. Validation of wearable textile electrodes for ECG monitoring. Heart and Vessels34, 7 ...

    2019 doi

  84. [93]

    Food and Drug Administration

    U.S. Food and Drug Administration. 2023. Device Classification Under Section 513(f)(2) (De Novo): DEN220091 — Dream Sock. FDA De Novo database entry and review memo. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/denovo.cfm?id=DEN220091 Accessed: 2026-01-05

    2023

  85. [94]

    Burcin Ustbas, Deniz Kilic, Ayhan Bozkurt, Mustafa Erkin Aribal, and Ozge Akbulut. 2018. Silicone-based composite materials simulate breast tissue to be used as ultrasonography training phantoms.Ultrasonics88 (2018), 9–15. doi:10.1016/j.ultras.2018.03.001

    2018 doi

  86. [95]

    Virag Varga, Gergely Vakulya, Alanson Sample, and Thomas R. Gross. 2018. Enabling Interactive Infrastructure with Body Channel Communication.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.1, 4, Article 169 (Jan. 2018), 29 pages. doi:10.1145/3161180

    2018 doi

  87. [96]

    Virag Varga, Marc Wyss, Gergely Vakulya, Alanson Sample, and Thomas R. Gross. 2018. Designing Groundless Body Channel Communication Systems: Performance and Implications. InProceedings of the 31st Annual ACM Symposium on User Interface Software and Technology(Berlin, Germany)(...

    2018 doi

  88. [97]

    Carolina Varon, John Morales, Jesús Lázaro, Michele Orini, Margot Deviaene, Spyridon Kontaxis, Dries Testelmans, Bertien Buyse, Pascal Borzée, Leif Sörnmo, Pablo Laguna, Eduardo Gil, and Raquel Bailón. 2020. A Comparative Study of ECG-derived Respiration in Ambulatory Monitori...

    2020 doi

  89. [98]

    Sutharanam Veerappan, Harel Rosen, William Craelius, David Curcie, Mark Hiatt, and Tom Hegyi. 2000. Spectral Analysis of Heart Rate Variability in Premature Infants with Feeding Bradycardia.Pediatric Research47, 5 (May 2000), 659–662. doi:10.1203/00006450- 200005000-00017

    2000 doi

  90. [99]

    Victora, Rajiv Bahl, Aluísio J

    Cesar G. Victora, Rajiv Bahl, Aluísio J. D. Barros, Giovanny V. A. França, Susan Horton, Julia Krasevec, Simon Murch, Mari Jeeva Sankar, Neff Walker, and Nigel C. Rollins. 2016. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect.The Lancet387, 100...

    2016 doi

  91. [100]

    Vollence. 2026. Vollence Silicone Breast Forms Fake Boobs for Mastectomy Prosthesis Transgender. https://www.amazon.com/Vollence- Mastectomy-Transwomen-Transgender-individuals/dp/B078MXW9DX/ Accessed 2026-01-30

    2026

  92. [101]

    Walmart. 2026. 3/8" (9.5mm) Heavy Black Knitted Elastic - US Made - 20 yards. https://www.walmart.com/ip/3-8-9-5mm-Heavy- Black-Knitted-Elastic-US-Made-20-yards/5344036545 Accessed: 2026-02-01

    2026

  93. [102]

    Yuna Watanabe, Natasha Yamane, Aarti Sathyanarayana, Varun Mishra, and Matthew S. Goodwin. 2026. Beyond Motion Artifacts: Optimizing PPG Preprocessing for Accurate Pulse Rate Variability Estimation. InCompanion of the 2025 ACM International Joint Conference on Pervasive and Ub...

    2026

  94. [103]

    Katie L Westerfield, Kristen Koenig, and Robert Oh. 2018. Breastfeeding: common questions and answers.American family physician 98, 6 (2018), 368–376. https://www.aafp.org/pubs/afp/issues/2018/0915/p368.html

    2018

  95. [104]

    Mackenzie DM Whipps and Jill R Demirci. 2021. The sleeper effect of perceived insufficient milk supply in US mothers.Public Health Nutrition24, 5 (2021), 935–941. doi:10.1017/S1368980020001482

    2021 doi

  96. [105]

    Christina Wolfe and Amy Jnah. 2024. Tracheoesophageal Fistula With Esophageal Atresia: A Case Series.Neonatal network43, 2 (2024). doi:10.1891/NN-2023-0051

    2024 doi

  97. [106]

    Junxi Xia, Doğaç Eldenk, Hongjun Xu, Yang Liu, and Stephen Xia. 2026. Short Paper: EarSleeve: Transforming Everyday Earphones into a 12-Lead ECG Sensing Platform. InProceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing System...

    2026

  98. [107]

    Chenhan Xu, Huining Li, Zhengxiong Li, Hanbin Zhang, Aditya Singh Rathore, Xingyu Chen, Kun Wang, Ming-chun Huang, and Wenyao Xu. 2021. CardiacWave: A mmWave-based Scheme of Non-Contact and High-Definition Heart Activity Computing.Proceedings of the ACM on Interactive, Mobile,...

    2021 doi

  99. [108]

    Koji Yatani and Khai N. Truong. 2012. BodyScope: A wearable acoustic sensor for activity recognition. InProceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp ’12). Association for Computing Machinery, New York, NY, USA, 341–350. doi:10.1145/2370216.2370269 Pr...

    2012

  100. [109]

    Takuro Yonezawa, Tomotaka Ito, and Hideyuki Tokuda. 2011. Transferring information from mobile devices to personal computers by using vibration and accelerometer. InProceedings of the 13th International Conference on Ubiquitous Computing(Beijing, China) (UbiComp ’11). Associat...

    2011

  101. [110]

    Shibo Zhang, Yuqi Zhao, Dzung Tri Nguyen, Runsheng Xu, Sougata Sen, Josiah Hester, and Nabil Alshurafa. 2020. NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Tec...

    2020 doi

  102. [111]

    Yang Zhang and Chris Harrison. 2015. Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology(Charlotte, NC, USA)(UIST ’15). Association for Computing Machiner...

    2015

  103. [112]

    Ng, Hrvoje Benko, and Chris Harrison

    Yang Zhang, Wolf Kienzle, Yanjun Ma, Shiu S. Ng, Hrvoje Benko, and Chris Harrison. 2019. ActiTouch: Robust Touch Detection for On-Skin AR/VR Interfaces. InProceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology(New Orleans, LA, USA)(UIST ’19). As...

    2019

  104. [113]

    Yang Zhang, Robert Xiao, and Chris Harrison. 2016. SkinTrack: Using the Body as an Electrical Waveguide for Continuous Finger Tracking on the Skin. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI). ACM, 1491–1503. doi:10.1145/2858036.2858082...

    2016

  105. [2024]

    doi:10.1371/journal.pdig.0000538

    QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.PLOS Digital Health3, 8 (08 2024), 1–19. doi:10.1371/journal.pdig.0000538

    2024 doi

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.