Journal
PHYSIOLOGICAL MEASUREMENT
Volume 43, Issue 2, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6579/ac5007
Keywords
electrodermal activity; electrodes; skin impedance
Categories
Funding
- European Research Council (ERC) under the European Union [ERC-2018 CoG-816564 ActionContraThreat]
- National Institute for Health Research (NIHR) UCLH Biomedical Research Centre
- Wellcome [203147/Z/16/Z]
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Electrodermal activity (EDA) measurement has been transferred from the laboratory to wearable devices, bringing along new applications and opportunities. Challenges remain for the quality of wearable EDA measurement, but ongoing developments in instrumentation, material technology, and data science may shorten the quality gap between wearable EDA and standardized recordings in the laboratory.
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.
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