Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study

Abstract

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4–10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement–disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.