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- W3094893237 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." @default.
- W3094893237 created "2020-11-09" @default.
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- W3094893237 date "2020-11-02" @default.
- W3094893237 modified "2023-10-04" @default.
- W3094893237 title "Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review" @default.
- W3094893237 doi "https://doi.org/10.20944/preprints202011.0043.v1" @default.
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