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Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review
Open accessAbstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.
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“Two components that contribute to measurable changes in EDA have been described. The fast galvanic skin responses, also referred to as phasic responses, act in the order of 0.5 to 5 s, whereas the tonic component, which expresses in level changes, acts in the order of minutes26,27”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [26]
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“Motion artifacts are a primary cause of bad signal quality, and research has suggested various methods for mitigating these26,39,40.”
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Metadata
- Authors
- Posada-Quintero, H. F., & Chon, K. H.
- Affiliations
- University of Connecticut (US)
- Corresponding author
- Chon, Ki H.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-15
- Volume
- 20
- Issue
- 2
- Pages
- 479
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7014446
- OpenAlex
- W3000402017
- Cited by
- 525
- References
- 75
- Citations by year
- 2026: 103 · 2025: 92 · 2024: 109 · 2023: 85 · 2022: 57 · 2021: 57 · 2020: 22
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Office of Naval Research (N00014-19-1-2209)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- electrodermal activity, sympathetic nervous system, sweat glands, psychophysiology, fatigue analysis, pain assessment, sleepiness, post-exercise recovery, epilepsy diagnosis, neuropathy, depression
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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