Unraveling Temporal Dependencies in Stress: Insights from Self-Reported Stress Data
Year: 2024
Type: conference-paper
Abstract: Understanding how stress evolves over time is crucial for improving stress detection research. This study examines temporal dependencies in self-reported stress data. We analyzed three self-reported stress datasets to explore how past and present stress levels correlate, utilizing the autocorrelation function (ACF). Our finding quantitatively showed that temporal dependencies in stress levels vary across participants, and the degree of these dependencies differs across datasets collected in different contexts. We provide some insights on how to consider temporal dependencies in self-reported stress for stress detection models, taking into account individual and contextual variations.
Authors: Heejeong Lim, Duri Lee, Uichin Lee
Institution: Korea Advanced Institute of Science and Technology
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4402721809