Cited by 105Non-Invasive Vital Sign MonitoringAdvanced Sensor and Energy Harvesting MaterialsWireless Body Area Networks

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index; none of the 52 sources its claims cite is checked for support yet.

Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings

Open access

Abstract

Significance: Monitoring vital signs for laboring women and their fetuses is foundational to the delivery of obstetrical care; however, monitoring platforms for pregnancy have undergone little innovation over the last several decades with many low-income settings lacking basic access. We report a new time-synchronized, flexible, and wireless sensor system applicable across the entire continuum of antepartum and postpartum care that provides continuous, comprehensive, and noninvasive monitoring (heart rate, respiratory rate, and pulse oxygenation) compatible with a wide range of mobile devices. This system offers advanced features such as continuous blood pressure, uterine electrohysterography, and automated body position classification. We further demonstrate the performance of this new system among pregnant individuals ( n = 576) in both high-resource settings and low-resource care settings.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper reports a time-synchronized, wireless, soft, flexible sensor network for comprehensive maternal–fetal pregnancy monitoring across antepartum and postpartum care. It was tested in 576 pregnant individuals in high-resource Chicago and low-resource Lusaka, with comparisons against gold-standard monitors for vital signs, fetal heart rate, uterine contraction, and continuous blood pressure surrogates.

Key findings

  • The system uses chest, limb, and abdominal sensors: chest and limb sensors operate for 24 h, the abdominal sensor for 8 h; the three sensors are time-synchronized with 1-ms timing accuracy and an SD of 3.6 ms over 24 consecutive hours.
  • In 13 healthy nonpregnant adults, compared with a Philips Intellivue MP50, mean differences were −0.09 bpm for HR (SD 0.95 bpm), −0.19% for SpO2 (SD 1.68%), and −0.45 breaths per minute for RR (SD 1.64 breaths per minute), within FDA guidelines for new cardiopulmonary monitoring platforms.
  • Fetal heart rate from the abdominal sensor compared with gold-standard Doppler in n = 10 laboring women across 3,213 single data points had a mean difference of 0.1 bpm (SD 1 bpm).
  • In the total cohort of 576 pregnant people, 91 (16%) were recruited in Chicago using all three sensors and 485 (84%) in Lusaka using maternal sensors only; more than 85% of surveyed laboring people (41 of 48 respondents) and all 10 surveyed obstetrical providers reported a positive or very positive experience.

Methods

  • Design and sample: field trials/validation among pregnant women at 25–41 wk gestation (n = 576), including 91 at Prentice Women’s Hospital in Chicago (59 nonstress testing, 32 active labor) and 485 in Lusaka, Zambia, as part of the LABOR study; additional validation included n = 13 healthy nonpregnant adults, n = 10 laboring women for FHR, and n = 3 healthy subjects for cold-pressor BP testing.
  • Sensor construction: four-layer flexible printed circuit boards for chest and limb sensors, two-layer for the abdominal sensor, packaged in silicone; chest sensor measures ECG, SCG, chest-wall movement for RR, and skin temperature; limb sensor measures PPG and skin temperature; abdominal sensor measures Doppler ultrasound, EHG, and ECG.
  • Analysis: real-time algorithms on mobile devices derived HR, SpO2, RR, temperature, FHR, uterine contraction, PAT-based BP, and body position; Bland–Altman comparisons were made against gold-standard systems; body position was classified using a Gaussian mixture model with n = 8 clusters merged into four postures; time-series heat maps were generated for HR, RR, SpO2, and derived SBP in the low-resource cohort (n = 485).

Limitations

  • The paper states that PAT-based BP measurements still require initial calibration with a gold-standard BP device, and this calibration step leads to further errors; further work is needed to validate PAT-based measurements for elevated BP following established guidelines with at least n = 85 subjects before wider clinical use.
  • The paper states that further development efforts are targeting enhancement of fetal ECG capabilities of the system.
  • Evident from the text: the low-resource cohort of 485 pregnant people in Lusaka used maternal sensors only (chest and limb), so fetal monitoring with the abdominal sensor was not validated in that setting; the abdominal sensor also has a shorter operation time (8 h) than the chest and limb sensors (24 h).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1073/pnas.2100466118

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1. Overview of the maternal fetal monitoring system. The on-body network of the maternal–fetal sensor system is visualized. (A and B) Photographs of the front and back side of the chest and limb sensors. The silicone encapsulation allows the sensors to be soft and stretchable while still operating wirelessly. (C) The abdominal sensor is designed to conform around the abdomen of the patient without the need for an external strap. (D) Individually, each sensor captures unique signals from the patient. (E) Together, the sensors constitute an on-body network capable of acquiring more advanced metrics.
Fig. 1. Overview of the maternal fetal monitoring system. The on-body network of the maternal–fetal sensor system is visualized. (A and B) Photographs of the front and back side of the chest and limb sensors. The silicone encapsulation allows the sensors to be soft and stretchable while still operating wirelessly. (C) The abdominal sensor is designed to conform around the abdomen of the patient without the need for an external strap. (D) Individually, each sensor captures unique signals from the patient. (E) Together, the sensors constitute an on-body network capable of acquiring more advanced metrics.CC BY · PubMed Central
Fig. 2. Maternal vital signs. The end-to-end data analytics of the sensor system is outlined. (A) Waveforms from the patient are obtained by the chest, limb, and abdominal sensors. (B) The raw signals are then processed to yield the representative vital signs of a clinical setting. (C) The patient’s body orientation is also identifiable through the use of an embedded IMU. (D–F) Our calculated metrics are statistically comparable to the gold standard used in modern hospitals.
Fig. 2. Maternal vital signs. The end-to-end data analytics of the sensor system is outlined. (A) Waveforms from the patient are obtained by the chest, limb, and abdominal sensors. (B) The raw signals are then processed to yield the representative vital signs of a clinical setting. (C) The patient’s body orientation is also identifiable through the use of an embedded IMU. (D–F) Our calculated metrics are statistically comparable to the gold standard used in modern hospitals.CC BY · PubMed Central
Fig. 3. Doppler-derived FHR and EMG-derived uterine contraction. Data analytics of the FHR and maternal uterine contraction are outlined. (A) The raw US Doppler signal is obtained by the abdominal sensor. We can identify the S1 and S2 waves and see the signal aligned with peaks indicative of fetal ECG. (B and C) Our calculated FHR is statistically comparable to the gold standard. (D) The raw biosignal is obtained by the abdominal sensor. We acquire two channels for sequential processing of the EMG signal. (E) Our calculated uterine contraction output is overlaid onto the gold standard.
Fig. 3. Doppler-derived FHR and EMG-derived uterine contraction. Data analytics of the FHR and maternal uterine contraction are outlined. (A) The raw US Doppler signal is obtained by the abdominal sensor. We can identify the S1 and S2 waves and see the signal aligned with peaks indicative of fetal ECG. (B and C) Our calculated FHR is statistically comparable to the gold standard. (D) The raw biosignal is obtained by the abdominal sensor. We acquire two channels for sequential processing of the EMG signal. (E) Our calculated uterine contraction output is overlaid onto the gold standard.CC BY · PubMed Central
Fig. 4. Continuous blood pressure correlation. The derivation of continuous BP from PAT and HR measurements. (A) In a cold-pressor test, we use the first 50 s of sampled data to derive linear coefficients for PAT. For each sample, we overlay the gold-standard SBP. (B) A Bland–Altman plot comparing the calibrated PAT to the gold-standard SBP from the cold-pressor test is presented. (C) An inverse relationship across all participants is derived between PAT and sampled BP cuff measurements. (D) n = 1 is shown for the converted systolic and diastolic BPs from PAT.
Fig. 4. Continuous blood pressure correlation. The derivation of continuous BP from PAT and HR measurements. (A) In a cold-pressor test, we use the first 50 s of sampled data to derive linear coefficients for PAT. For each sample, we overlay the gold-standard SBP. (B) A Bland–Altman plot comparing the calibrated PAT to the gold-standard SBP from the cold-pressor test is presented. (C) An inverse relationship across all participants is derived between PAT and sampled BP cuff measurements. (D) n = 1 is shown for the converted systolic and diastolic BPs from PAT.CC BY · PubMed Central
Fig. 5. Heat-map analysis of maternal vital signs for laboring women in low-resource settings. A heat map of each vital signal is generated for all 485 participants. (Left) Plot (in red–black) for each vital is a heat map without normalization as the majority of labors last less than 5 h. (Right) Plot (in yellow–green) is normalized by frequency with a 4-h window to illustrate average vital signs over longer labors.
Fig. 5. Heat-map analysis of maternal vital signs for laboring women in low-resource settings. A heat map of each vital signal is generated for all 485 participants. (Left) Plot (in red–black) for each vital is a heat map without normalization as the majority of labors last less than 5 h. (Right) Plot (in yellow–green) is normalized by frequency with a 4-h window to illustrate average vital signs over longer labors.CC BY · PubMed Central
Fig. 6. Body position and vital signs. Small, but clear, differences are seen in maternal vital signs based on body position for HR, SpO2, RR, and SBP derived from PAT. A vertical line is depicted in each of the vital sign distribution plots to denote the mean value for the specific body position.
Fig. 6. Body position and vital signs. Small, but clear, differences are seen in maternal vital signs based on body position for HR, SpO2, RR, and SBP derived from PAT. A vertical line is depicted in each of the vital sign distribution plots to denote the mean value for the specific body position.CC BY · PubMed Central

Verification

Claims the paper makes

32 Not checked yet · 30 Own result · 16 No basis given · 6 Own interpretation · 2 Own method

Verify all 52 references — free account
  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 17
  • The quote only partly states this claim. 1
47 of 47
  • The paper relies on this without support.

    The S1 and S2 peaks in the Doppler ultrasound signal represent closure of the fetal heart valves.

    “The first peak “S1” represents closure of the tricuspid and mitral valves of the fetal heart, and the second peak “S2” represents closure of the pulmonary semilunar valves of the fetal heart (Audio S1).”
  • The paper relies on this without support.

    The electrohysterography contraction can be converted to millimeters of mercury or pressure with a simple calibration.

    “The EHG contraction can be converted to millimeters of mercury (mmHg) or pressure with a simple calibration.”
  • The paper relies on this without support.

    Of all wireless pregnancy monitoring devices currently approved for clinical use, none incorporates maternal vital signs.

    “Perhaps most notable, of all the wireless pregnancy monitoring devices currently approved for clinical use, none incorporates maternal vital signs.”
  • The paper relies on this without support.

    Assessing maternal vital signs such as HR, RR, SpO2, and BP requires additional systems wired to the body and configured to another monitoring system that is often not interoperable.

    “Assessing maternal vital signs such as HR, RR, SpO2, and BP requires additional systems wired to the body and configured to another monitoring system that is often not interoperable.”
  • The paper relies on this without support.

    Research on the effects and benefits of laboring positions has been largely limited because of inability to monitor and collect data in clinical settings with fidelity.

    “Despite these studies, research on the effects and benefits of laboring positions has been largely limited because of inability to monitor and collect data in clinical settings with fidelity.”
  • The paper relies on this without support.

    In both high- and low-resource settings the burden placed on clinical staff in order to obtain physiologic information on a woman and her fetus is significant.

    “In both high- and low-resource settings the burden placed on clinical staff in order to obtain physiologic information on a woman and her fetus is significant.”
6 of 6

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1 of 1

Citations

Cites

Show all 56 references

Research works printed but not linked: 1

  1. World Health Organization, Trends in Maternal Mortality 2000 to 2017: Estimates by WHO, UNICEF, UNFPA (World Bank Group and the United Nations Population Division, 2019).

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Metadata

Authors
Ryu, D., Kim, D. H., Price, J. T., Lee, J. Y., Chung, H. U., Allen, E., Walter, J. R., Jeong, H., Cao, J., Kulikova, E., Abu-Zayed, H., Lee, R., Martell, K. L., Zhang, M., Kampmeier, B. R., Hill, M., Lee, J., Kim, E., Park, Y., Jang, H., Arafa, H., Liu, C., Chisembele, M., Vwalika, B., Sindano, N., Spelke, M. B., Paller, A. S., Premkumar, A., Grobman, W. A., Stringer, J. S. A., Rogers, J. A., & Xu, S.
Affiliations
  • Sibel (United States) (US)
  • University of North Carolina at Chapel Hill (US)
  • Northwestern University (US)
  • Sibel Inc., Niles, IL 60714;
  • University Teaching Hospital (ZM)
  • University of Zambia (ZM)
  • University of North Carolina Global Projects–Zambia, Lusaka 10101, Zambia;
Corresponding author
  • Stringer, Jeffrey S. A.
  • Rogers, John A.
  • Xu, Shuai
Venue
Proceedings of the National Academy of Sciences
Published
2021-05-10
Volume
118
Issue
20
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W3161046902
Cited by
105
References
56
Citations by year
2026: 16 · 2025: 23 · 2024: 18 · 2023: 29 · 2022: 14 · 2021: 5
FWCI
5.01
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
Bill and Melinda Gates Foundation (OPP1193311)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
Keywords
pregnancy monitoring, wireless sensors, flexible sensors, vital signs monitoring, antepartum care, postpartum care, fetal monitoring, continuous blood pressure monitoring, resource-limited settings, mobile health, non-invasive monitoring
MeSH terms
Wearable Electronic Devices, Female, Health Resources, Heart Rate, Fetal, Humans, Monitoring, Physiologic, Pregnancy, Uterine Contraction, Vital Signs, Wireless Technology

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