# Stress Analysis with Dimensions of Valence and Arousal in the Wild
###### Abstract  
In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition.  
stress recognition; valence; arousal; affective computing; Korean movies +
Footnote †: journal: Medical  
studies is to understand stress better and build a computational mechanism to detect stressful situations. For example, assessing employees' stress levels through monitoring systems enables the adjustment of working conditions, thereby improving the performance of the employees [8; 9].  
To identify various levels of stress, we present a new publicly available dataset named Stress Analysis with Dimensions of Valence and Arousal in Wild (SADVAW), which focuses on stress in 9 different levels of expression and the representation of stress in valence-arousal space. In this paper, we present three main contributions:  
* Firstly, we analyze the relationship between stress and continuous dimensions of valence and arousal.
* The second, the SADVAW dataset, which is constructed to evaluate multiple stress levels in continuous dimensions of valence and arousal based on facial features. The SADVAW dataset consists of 1236 video clips extracted from 41 Korean movies. Video clips are extracted from many characters with different backgrounds, closer to the real-world environment. The video clips were evaluated on a 9-level scale for each stress, valence, and arousal class.
* The third contribution is developing a baseline model based on deep learning techniques for stress level detection. In particular, we first detect and extract human faces using TinyFace [12] model. Then, we use ResNet [13] (pp. 770-778) to extract an important feature vector for each frame of the video clip. The sequence of features is trained by Long Short-Term Memory (LSTM) [14], followed by fully connected layers to predict the stress, arousal, and valence level. Based on the SADVAW dataset, we determined the correlation of stress with valence and arousal. Furthermore, we aim to use this analysis for stress detection systems through images/videos captured from real-world situations.  
The remainder of this paper is organized as follows. Section 2 discusses the related works on data sets for emotion recognition. Section 3 provides details of the dataset construction process and dataset statistics. Section 4 describes the details of the baseline model, along with experiments and a discussion of the results. Finally, Section 5 presents the conclusions drawn from the study.