Key result
Deep learning outperforms traditional ML for stress detection with a ~0.27 Kappa score.
Why the study?
It remains an open research question if stress detection models trained on datasets recorded under intense stressors can effectively detect stress in different contexts such as user experience evaluation.
Deep learning models using physiological signals (EDA and ST) can effectively detect stress with high accuracy and show high agreement in user experience evaluation contexts.
DL models may aid stress detection in UX settings; leaves open validation and clinical translation.
Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.
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Liapis et al. (2021) studied Stress (n=45). Deep learning model with categorical variables vs. Traditional machine learning classifiers (e.g., Q-SVM) was evaluated on Interrater reliability (Kappa coefficient) for stress detection in a UX evaluation context (95% CI 0.09-0.45, p=<0.05). A deep learning model combining continuous variables and entity embeddings achieved a Kappa of 0.27 for stress detection in a UX evaluation context, outperforming traditional machine learning classifiers.
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