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Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors.

Rummana Bari¹, Md Mahbubur Rahman², Nazir Saleheen²

¹University of Memphis, Electrical and Computer Engineering, Memphis, TN, 38152, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

|June 8, 2021

Summary

This study introduces a model to detect stressful conversations using wearable sensors. The system identifies physiological arousal and recovery patterns, achieving 83% accuracy in detecting stressful conversations within minutes.

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Area of Science:

Human-computer interaction
Physiological computing
Affective computing

Background:

Stressful conversations are common daily stressors impacting health and relationships.
Existing methods lack automated, real-time detection of conversation-induced stress.

Purpose of the Study:

To develop and validate a model for automatic detection of stressful conversations using wearable sensors.
To introduce and utilize the concept of stress cycles for improved stress event classification.

Main Methods:

Collected physiological and inertial sensor data from cohabiting couples in lab and field studies.
Developed novel features from physiological arousal and recovery patterns (stress cycles) and hand gestures.
Trained and tested a machine learning model on field data from 38 participants.

Main Results:

The model accurately distinguishes stressful conversations from other stressors using stress cycle features.
Distinct patterns in physiological responses and hand gestures were identified during stressful conversations.
Achieved an F1-score of 0.83 in detecting stressful conversations.

Conclusions:

Wearable sensors and stress cycle analysis enable accurate, automated detection of stressful conversations.
Early detection facilitates timely interventions to mitigate negative impacts of stressful conversations.
The model's ability to detect stress within minutes shows promise for real-world applications.