Using activity-related behavioural features towards more effective automatic stress detection

Pietro Cipresso, Andrea Gaggioli, Giuseppe Riva, Dimitris Giakoumis, Anastasios Drosou, Dimitrios Tzovaras, George Hassapis

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

42 Citazioni (Scopus)

Abstract

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.
Lingua originaleEnglish
pagine (da-a)1-16
Numero di pagine16
RivistaPLoS One
Volume7
DOI
Stato di pubblicazionePubblicato - 2012

Keywords

  • activity
  • stress detection

Fingerprint

Entra nei temi di ricerca di 'Using activity-related behavioural features towards more effective automatic stress detection'. Insieme formano una fingerprint unica.

Cita questo