Stress Detection Using Wearable Physiological and Sociometric Sensors

Oscar Martinez Mozos; Virginia Sandulescu; Sally Andrews; David Ellis; Nicola Bellotto; Radu Dobrescu; Jose Manuel Ferrandez

cs.LGApr 14, 2026

Stress Detection Using Wearable Physiological and Sociometric Sensors

Authors: Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Radu Dobrescu, Jose Manuel Ferrandez

Organizations: Dept. of Electronics, Computer Technology and Projects, Polytechnic University of Cartagena, Plaza del Hospital, n1, 30202, Cartagena, Spain · Faculty of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest, 060042, Romania · Division of Psychology, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, UK · Department of Psychology, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK · School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, LN67TS, UK

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 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.

Figures & tables

Panel of Figure 1 — Figure 1: Example signals recorded during the TSST sessions for participant $P_{6}$ (left column), and participant $P_{16}$ (right column). The x-axis additionally presents the task the person was carrying out at each instant: NT 1 (First Neutral Task), PP (Presentation Preparation), PS (Public Speaking), Cognitive Task (CG), NT 2 (Second Neutral Task).

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Automatic Detection of Stress from Speech in the Trier Social Stress Test

Hanna Drimalla, Wieland R. Cremer, Christine Kraus, Oliver T. Wolf

Human-Centered Artificial Intelligence Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. · Department of Cognitive Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany.

Speech Emotion Recognition Affective Computing

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Automatically detecting stress in speech provides an unobtrusive way to gain insights relevant to behavioral research or clinical assessment. This study investigates the automatic differentiation between a stressful and non-stressful situation, and the prediction of physiological and affective stress responses. Speech data was collected from 50 participants who either completed the Trier Social Stress Test (TSST) or a non-stressful control condition. With a processing pipeline that included speaker diarization and machine learning models, we achieved stress detection performance significantly above a mean baseline. Moreover, relevant physiological and affective stress responses were partially predictable from acoustic-prosodic features. Feature-importance analyses identified the most informative predictors contributing to model performance. The findings demonstrate that speech can serve as a meaningful and unobtrusive indicator of multiple dimensions of the human stress response.

Abstract

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