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Sensors 2017, 17(10), 2324;

Electrodermal Activity Sensor for Classification of Calm/Distress Condition

Instituto de Tecnologías Audiovisuales, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
Instituto de Investigación en Informática, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 6 October 2017 / Accepted: 10 October 2017 / Published: 12 October 2017
(This article belongs to the Section Physical Sensors)
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This article introduces a new and unobtrusive wearable monitoring device based on electrodermal activity (EDA) to be used in health-related computing systems. This paper introduces the description of the wearable device capable of acquiring the EDA of a subject in order to detect his/her calm/distress condition from the acquired physiological signals. The lightweight wearable device is placed in the wrist of the subject to allow continuous physiological measurements. With the aim of validating the correct operation of the wearable EDA device, pictures from the International Affective Picture System are used in a control experiment involving fifty participants. The collected signals are processed, features are extracted and a statistical analysis is performed on the calm/distress condition classification. The results show that the wearable device solely based on EDA signal processing reports around 89% accuracy when distinguishing calm condition from distress condition. View Full-Text
Keywords: wearable; electrodermal activity; arousal; valence; distress; calmness wearable; electrodermal activity; arousal; valence; distress; calmness

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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 (CC BY 4.0).

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Zangróniz, R.; Martínez-Rodrigo, A.; Pastor, J.M.; López, M.T.; Fernández-Caballero, A. Electrodermal Activity Sensor for Classification of Calm/Distress Condition. Sensors 2017, 17, 2324.

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