From Physiological Measures to an Automatic Recognition System of Stress - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

From Physiological Measures to an Automatic Recognition System of Stress

Abstract

Evaluation of stress is mainly based on standardized scales. However, fill out questionnaires can be incompatible with several situations (e.g. during chirurgical intervention) and offers only subjective and punctual data. Physiological measures, which provided real-time and objective data, can be used to cope with these constrains. To be effective, physiological data need to be related to human feeling. One solution is to build an automatic recognition system of stress based on supervised machine learning. Thereby, to acquire physiological data, we built stressful situation in laboratory. From physiological data (respiratory, cardiac and electrodermal measurement) of 24 participants, we built a model that recognizes stress with an accuracy of 70%.
Fichier principal
Vignette du fichier
Extended_abstract_HCI16_NM_JM _ final.pdf (142.06 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01525673 , version 1 (29-05-2017)

Identifiers

Cite

Nicolas Martin, Jean-Marc Diverrez. From Physiological Measures to an Automatic Recognition System of Stress. HCI International 2016, Jul 2016, Toronto, Canada. pp.139 - 176, ⟨10.1007/978-3-319-40542-1_27⟩. ⟨hal-01525673⟩
83 View
185 Download

Altmetric

Share

Gmail Facebook X LinkedIn More