arXiv:2402.15513v1 Announce Type: new
Abstract: Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.
Expert Commentary: Evaluating the Generalizability of Physiological Features in Stress Detection
In recent years, machine learning (ML) techniques have shown promise in detecting anxiety and stress using physiological signals. However, it is important to determine whether these ML models are truly learning features specific to stress or if they are detecting a more general state of high arousal. This article presents a study that aims to address this ambiguity by evaluating the generalizability of physiological features associated with anxiety and stress to other high-arousal emotions.
The study examines features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from three different datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. By analyzing these features, the researchers seek to understand whether they are specific to anxiety or applicable to other high-arousal emotions.
To evaluate the generalizability of these features, the researchers conducted a statistical regression analysis in addition to various cross-validation techniques. They used several classifiers, including Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of these models to train and test their models on different combinations of stress and arousal datasets.
The findings from this study provide valuable insights into the nature of stress detection through physiological signals. The results indicate that models trained on datasets related to arousal perform well on stress datasets, and vice versa. This suggests that the evaluated models may be identifying emotional arousal rather than specifically detecting stress.
This is a significant contribution to the field as it is the first cross-corpus evaluation that explores the relationship between stress and arousal using ECG and EDA signals. By highlighting the generalizability of stress detection methods, this work advances our understanding of the broader implications of physiological signal analysis in the field of multimedia information systems.
The concepts explored in this study have significant interdisciplinary relevance. The field of multimedia information systems encompasses various disciplines such as computer science, psychology, and human-computer interaction. By applying machine learning techniques to physiological signals, researchers bridge the gap between these disciplines, paving the way for innovative applications in areas like augmented reality, virtual realities, and artificial reality.
Animations in virtual and augmented reality environments can be intelligently adjusted based on the user’s stress or arousal levels. For example, if a user is becoming overly stressed, the virtual environment can adapt by providing calming visuals or sounds to alleviate their anxiety. Similarly, in artificial reality applications such as medical simulations, the system can respond to the user’s stress levels to provide personalized feedback and guidance.
Overall, this study contributes to the broader field of multimedia information systems by providing insights into the generalizability of stress detection methods and highlighting the interdisciplinary nature of the concepts explored. It opens up possibilities for integrating physiological signal analysis into various multimedia applications, paving the way for more immersive and personalized experiences in virtual, augmented, and artificial realities.