Evidence map›Paper›PMID 38765485›Full record

ArticleIEEE internet of things journal2024

A Wrist-Worn Internet of Things Sensor Node for Wearable Equivalent Daylight Illuminance Monitoring.

Navid Mohammadian, Altug Didikoglu, Christopher Beach, Paul Wright, Joshua W Mouland, Franck P Martial, Sheena Johnson, Martie van Tongeren, Timothy M Brown, Robert J Lucas and 1 more

Abstract read
In one paragraph

Article in IEEE internet of things journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  11. Associations between light exposure and sleep timing and sleepiness while awake in a sample of UK adults in everyday life.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  12. Human-Centric Lighting Research and Policy in the Melanopsin Age.Policy insights from the behavioral and brain sciences · 2023
    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

11 authors.

Navid MohammadianHenry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-1737-1321
Altug DidikogluDivision of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-5582-6956
Christopher BeachHenry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0003-4964-3173
Paul WrightDepartment of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-4314-0754
Joshua W MoulandDivision of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0003-4309-3668
Franck P MartialDivision of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.
Sheena JohnsonPeople, Management and Organisation Division, Alliance Manchester Business SchoolThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-6687-2333
Martie van TongerenDivision of Population Health, Health Services Research and Primary Care, School of Health SciencesThe University of Manchester M13 9PL Manchester U.K.
Timothy M BrownDivision of Diabetes, Endocrinology and Gastroenterology, School of Medical SciencesThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-5625-4750
Robert J LucasDivision of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0002-1088-8029
Alexander J CassonHenry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.ORCID https://orcid.org/0000-0003-1408-1190

Funding

Wellcome Trust
6 · The paper itself

Abstract

Light exposure is a vital regulator of physiology and behavior in humans. However, monitoring of light exposure is not included in current wearable Internet of Things (IoT) devices, and only recently have international standards defined [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of EDI measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured [Formula: see text]-opic EDI results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT-based lighting control.

Indexed as

Light sensinglow-power electronicswearable devices

Identifiers

PMID38765485
PMCPMC11100858

What OpenQuestion holds

Textmetadata
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.