Integrating Multimodal Affective Signals for Stress Detection from Audio-Visual Data

Stress detection in real-world settings presents significant challenges due to the complexity of human emotional expression influenced by biological, psychological, and social factors. While traditional methods like EEG, ECG, and EDA sensors provide direct measures of physiological responses, they are unsuitable for everyday environments due to their intrusive nature. Therefore, using non-contact, commonly available sensors like cameras and microphones to detect stress would be helpful. In this work, we use stress indicators from four key affective modalities extracted from audio-visual data: facial expressions, vocal prosody, textual sentiment, and physical fidgeting. To achieve this, we first labeled 353 video clips featuring individuals in monologue scenarios discussing personal experiences, indicating whether or not the individual is stressed based on our four modalities. Then, to effectively integrate signals from the four modalities, we extract stress signals from our audio-visual data using unimodal classifiers. Finally, to explore how the different modalities would interact to predict if a person is stressed, we compare the performance of three multimodal fusion methods: intermediate fusion, voting-based late fusion, and learning-based late fusion. Results indicate that combining multiple modes of information can effectively leverage the strengths of different modalities and achieve an F1 score of 0.85 for binary stress detection. Moreover, an ablation study shows that the more modalities are integrated, the higher the F1 score for detecting stress across all fusion techniques, demonstrating that our selected modalities possess complementary stress indicators.