Work

Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors

Year: 2020

Type: article

Abstract: Stressful conversation is a frequently occurring stressor in our daily life. Stressors not only adversely affect our physical and mental health but also our relationships with family, friends, and coworkers. In this paper, we present a model to automatically detect stressful conversations using wearable physiological and inertial sensors. We conducted a lab and a field study with cohabiting couples to collect ecologically valid sensor data with temporally-precise labels of stressors. We introduce the concept of stress cycles, i.e., the physiological arousal and recovery, within a stress event. We identify several novel features from stress cycles and show that they exhibit distinguishing patterns during stressful conversations when compared to physiological response due to other stressors. We observe that hand gestures also show a distinct pattern when stress occurs due to stressful conversations. We train and test our model using field data collected from 38 participants. Our model can determine whether a detected stress event is due to a stressful conversation with an F1-score of 0.83, using features obtained from only one stress cycle, facilitating intervention delivery within 3.9 minutes since the start of a stressful conversation.

Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

DOI: 10.1145/3432210

Authors: Rummana Bari, Md. Mahbubur Rahman, Nazir Saleheen, Megan Battles Parsons, Eugene H. Buder, Santosh Kumar

Institution: University of Memphis

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute on Minority Health and Health Disparities