Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection

• Published in: PloS one Vol. 7; no. 9; p. e43571
• Main Authors: Giakoumis, Dimitris, Drosou, Anastasios, Cipresso, Pietro, Tzovaras, Dimitrios, Hassapis, George, Gaggioli, Andrea, Riva, Giuseppe
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.09.2012; Public Library of Science (PLoS)
• Subjects: Accelerometers; Adult; Algorithms; Behavior; Biosensors; Clinical medicine; Cognition & reasoning; Computer engineering; Data processing; Detectors; EKG; Electrocardiography; Emotions; Engineering; Feature extraction; Female; Galvanic Skin Response; Health aspects; Human Activities; Human-computer interaction; Humans; Informatics; Male; Medicine; Models, Statistical; Physiology; Psychology; Signal processing; Skin; Social and Behavioral Sciences; Stress; Stress, Psychological - diagnosis; Stresses; Studies; Surveys and Questionnaires; Telematics; Tension headache; Videotape Recording; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0043571

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.