THE ACCURACY OF ELECTRODERMAL ACTIVITY SENSORS IN STRESS DETECTION: A NARRATIVE REVIEW
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Abstract
This study aimed to: 1) evaluate the accuracy of stress detection devices based on electrodermal activity (EDA), with emphasis on accuracy metrics and classification performance, and 2) synthesize multidimensional factors influencing the effectiveness of stress state classification. This research employed a narrative review approach, systematically retrieving data from academic databases and analyzing the findings through structured extraction and synthesis of relevant studies.
The findings revealed that: 1) EDA demonstrates strong potential for detecting and classifying stress states, with overall accuracy ranging from 82.4% to 98.8%, and reaching a maximum of 94.8% in males and 98.8% in females, particularly in cognitively demanding contexts such as Human–Computer Interaction (HCI), reflecting the sensitivity of EDA signals to autonomic nervous system activity; 2) the accuracy of measurement is dynamic and influenced by multiple dimensions, including biological factors (e.g., sex and age), physiological factors (e.g., skin temperature and humidity), technical factors (e.g., sensor placement and signal processing methods), as well as the nature of stress-inducing stimuli. However, EDA has inherent limitations due to its responsiveness to non-specific stimuli (e.g., physical activity or emotional arousal), which may introduce variability and affect result interpretation..
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