Current trends and opportunities in the methodology of electrodermal activity measurement.


Journal

Physiological measurement
ISSN: 1361-6579
Titre abrégé: Physiol Meas
Pays: England
ID NLM: 9306921

Informations de publication

Date de publication:
04 03 2022
Historique:
received: 01 10 2021
accepted: 28 01 2022
pubmed: 29 1 2022
medline: 19 3 2022
entrez: 28 1 2022
Statut: epublish

Résumé

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Identifiants

pubmed: 35090148
doi: 10.1088/1361-6579/ac5007
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Department of Health
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 203147/Z/16/Z
Pays : United Kingdom

Informations de copyright

© 2022 Institute of Physics and Engineering in Medicine.

Auteurs

Christian Tronstad (C)

Department of Clinical and Biomedical Engineering, Oslo University Hospital, Norway.

Maryam Amini (M)

Department of Physics, University of Oslo, Norway.

Dominik R Bach (DR)

Wellcome Centre for Human Neuroimaging and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom.

Ørjan G Martinsen (ØG)

Department of Clinical and Biomedical Engineering, Oslo University Hospital, Norway.
Department of Physics, University of Oslo, Norway.

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Classifications MeSH