Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Version 1 : Received: 31 October 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (13:37:08 CET)

How to cite: Sánchez-Reolid, R.; López, M.T.; Fernández-Caballero, A. Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review. Preprints 2020, 2020110043 (doi: 10.20944/preprints202011.0043.v1). Sánchez-Reolid, R.; López, M.T.; Fernández-Caballero, A. Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review. Preprints 2020, 2020110043 (doi: 10.20944/preprints202011.0043.v1).

Abstract

Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

Subject Areas

Electrodermal activity; Stress detection; Machine learning; Scoping review

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.