Next generation of non-contact and wireless vital sign monitoring technology in the neonatal intensive care unit: a systematic review
Bibliographic record
Abstract
BACKGROUND: Current vital sign monitoring uses skin sensors connected to monitors via wires. Emerging technologies include non-contact and wireless wearable systems. This systematic review aims to determine the current stage of development of these technologies and the prospect for clinical translation. METHODS: A search on Medline, Embase, Cochrane, Scopus, and Engineering Village was conducted for studies published between January 2014 and August 2024. Two reviewers independently screened articles and extracted data on technology, signals and feasibility, safety, and accuracy outcomes. Risk of bias was assessed using the QUADAS-2; quantitative and qualitative analyses were conducted. RESULTS: Sixty observational studies were included: 43 (72%) non-contact and 17 (28%) wireless sensors. All used a reference sensor, with a median sample of 10 patients (IQR: 6-29); and median participant characteristics were moderately preterm infants around 34 weeks of age (IQR: 31-35). Studies typically monitored a single vital sign using offline data processing with good accuracy. Risk of bias and applicability concerns were driven by small samples, unclear participant selection, and limited reporting. CONCLUSION: Next generation non-contact and wireless wearable technologies have the potential to enhance neonatal vital sign monitoring, but research addressing limitations and exploring feasibility and safety are needed. Standardized reporting frameworks and greater transparency are necessary for comparisons across studies. IMPACT: Most emerging technologies are non-contact technologies monitoring one vital sign, usually respiratory rate (RR), and a minority were wireless wearable sensors monitoring more than one vital sign usually heart rate and RR and showed good accuracy. Studies had small sample sizes, short recording durations, and exclusively focused on accuracy, and often missing important information about participants and study methodology. Wireless and non-contact technologies show promise, and this review provides recommendations to improve study design, extend recording durations, and enhance transparency in reporting and participant selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".