Stress detection using wearable physiological and sociometric sensors

Martinez-Mozos, Oscar and Sandulescu, Virginia and Andrews, Sally and Ellis, David Alexander and Bellotto, Nicola and Dobrescu, Radu and Ferrandez, Jose Manuel (2017) Stress detection using wearable physiological and sociometric sensors. International Journal of Neural Systems, 27 (2). ISSN 0129-0657

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Abstract

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Neural Systems
Additional Information:
Preprint of an article published in International Journal of Neural Systems, 27, 2, 2017, 1650041 http://dx.doi.org/10.1142/S0129065716500416 © copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1705
Subjects:
ID Code:
79449
Deposited By:
Deposited On:
10 May 2016 14:26
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Apr 2020 03:25