title: Stress Detection Using Wearable Physiological and Sociometric Sensors.
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 [Formula: see text]-nearest neighbor. 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.
authors.affiliation: 1 DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain.
authors.forename: Oscar Martinez
authors.initials: OM
authors.lastname: Mozos
authors.affiliation: 2 Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania.
authors.forename: Virginia
authors.initials: V
authors.lastname: Sandulescu
authors.affiliation: 3 Division of Psychology, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, UK.
authors.forename: Sally
authors.initials: S
authors.lastname: Andrews
authors.affiliation: 4 Department of Psychology, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK.
authors.forename: David
authors.initials: D
authors.lastname: Ellis
authors.affiliation: 5 School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, LN67TS, UK.
authors.forename: Nicola
authors.initials: N
authors.lastname: Bellotto
authors.affiliation: 2 Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania.
authors.forename: Radu
authors.initials: R
authors.lastname: Dobrescu
authors.affiliation: 1 DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain.
authors.forename: Jose Manuel
authors.initials: JM
authors.lastname: Ferrandez
mesh_terms: D000293:Adolescent; D000328:Adult; D001007:Anxiety / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology; D003071:Cognition / Q000502:physiology; D005260:Female; D005385:Fingers / Q000503:physiopathology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009483:Neuropsychological Tests; D011569:Psychiatric Status Rating Scales; D013060:Speech / Q000502:physiology; D013315:Stress, Psychological / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D059015:Wireless Technology / Q000295:instrumentation*; D055815:Young Adult
keywords: Activity monitoring; assistive technologies; physiology; sensors; signal classification; sociometric badges; stress; stress detection; wearable technology