Evidence map›Paper›PMID 34786293›Full record

ArticleIEEE access : practical innovations, open solutions2020

Wearables for the Next Pandemic.

Ava Hedayatipour, Nicole Mcfarlane

Abstract read
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ava HedayatipourDepartment of Electrical EngineeringCalifornia State University Long Beach CA 90840 USA.
Nicole McfarlaneDepartment of Electrical Engineering and Computer ScienceThe University of Tennessee Knoxville TN 37996 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper reviews the current state of the art in wearable sensors, including current challenges, that can alleviate the loads on hospitals and medical centers. During the COVID-19 Pandemic in 2020, healthcare systems were overwhelmed by people with mild to severe symptoms needing care. A careful study of pandemics and their symptoms in the past 100 years reveals common traits that should be monitored for managing the health and economic costs. Cheap, low power, and portable multi-modal-sensors that detect the common symptoms can be stockpiled and ready for the next pandemic. These sensors include temperature sensors for fever monitoring, pulse oximetry sensors for blood oxygen levels, impedance sensors for thoracic impedance, and other state sensors that can be integrated into a single system and connected to a smartphone or data center. Both research and commercial medically approved devices are reviewed with an emphasis on the electronics required to realize the sensing. The performance characteristics, such as accuracy, power, resolution, and size of each sensor modality are critically examined. A discussion of the characteristics, research challenges, and features of an ideal integrated wearable system is also presented.

Indexed as

Bioimpedancecircuits and systemsCOVID-19influenzapandemicpulse oximetrysensorstemperature sensorwearable devices

Identifiers

PMID34786293
PMCPMC8545280

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.