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

Light sensing low-power electronics wearable devices

Journal

IEEE internet of things journal
ISSN: 2327-4662
Titre abrégé: IEEE Internet Things J
Pays: United States
ID NLM: 101704157

Informations de publication

Date de publication:
01 May 2024
Historique:
received: 20 05 2023
revised: 13 11 2023
accepted: 03 01 2024
medline: 20 5 2024
pubmed: 20 5 2024
entrez: 20 5 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38765485
doi: 10.1109/JIOT.2024.3355330
pmc: PMC11100858
doi:

Types de publication

Journal Article

Langues

eng

Pagination

16148-16157

Informations de copyright

2327-4662 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Auteurs

Navid Mohammadian (N)

Henry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.

Altug Didikoglu (A)

Division of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.

Christopher Beach (C)

Henry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.

Paul Wright (P)

Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.

Joshua W Mouland (JW)

Division of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.

Franck P Martial (FP)

Division of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.

Sheena Johnson (S)

People, Management and Organisation Division, Alliance Manchester Business SchoolThe University of Manchester M13 9PL Manchester U.K.

Martie van Tongeren (M)

Division of Population Health, Health Services Research and Primary Care, School of Health SciencesThe University of Manchester M13 9PL Manchester U.K.

Timothy M Brown (TM)

Division of Diabetes, Endocrinology and Gastroenterology, School of Medical SciencesThe University of Manchester M13 9PL Manchester U.K.

Robert J Lucas (RJ)

Division of Neuroscience, School of Biological SciencesThe University of Manchester M13 9PL Manchester U.K.

Alexander J Casson (AJ)

Henry Royce Institute for Advanced Materials and the Department of Electrical and Electronic EngineeringSchool of EngineeringThe University of Manchester M13 9PL Manchester U.K.

Classifications MeSH