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. Preprints2020, 2020110043. https://doi.org/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. https://doi.org/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. Preprints2020, 2020110043. https://doi.org/10.20944/preprints202011.0043.v1
APA Style
Sánchez-Reolid, R., López, M.T., & Fernández-Caballero, A. (2020). Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review. Preprints. https://doi.org/10.20944/preprints202011.0043.v1
Chicago/Turabian Style
Sánchez-Reolid, R., María T. López and Antonio Fernández-Caballero. 2020 "Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review" Preprints. https://doi.org/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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
