MAD: A Multimodal Physiological and Self-Reported Dataset for Anxiety Research from a Low-to-Middle-Income Country

Wearable sensors provide a promising approach for monitoring physiological parameters, offering valuable insights into mental health conditions, including Social Anxiety Disorder (SAD). Early detection of SAD using physiological data can facilitate timely interventions, yet the development of robust anxiety detection models requires high-quality datasets. While real-world studies provide ecological validity, controlled studies ensure structured, high-quality data with minimal missing values, making them ideal for developing generalized and personalized anxiety detection models.; AB@Existing publicly available datasets related to anxiety research are limited to developed nations and focus on one or two anxiety-inducing activities. However, cultural differences significantly influence how anxiety is experienced and expressed, highlighting the need for datasets from diverse populations. This work presents MAD, a novel dataset collected in a low-to-middle-income country that addresses this gap. Our study involved participants engaging in three anxiety-inducing activities—speech, group discussion, and interview—each structured into three phases: anticipation, performance, and reflection. Physiological data were collected using wearable sensors, including electrocardiogram, electrodermal activity, and photoplethysmography, along with self-reported anxiety levels.; AB@Our dataset (N = 97) is unique in its inclusion of multiple anxiety-inducing activities, comprehensive phase-wise assessment, and representation of an underrepresented population. It provides a valuable resource for developing generalizable anxiety detection models, designing personalized interventions, and studying cultural variations in anxiety responses. By making MAD available, we aim to facilitate future research in machine learning-based mental health analysis, cross-cultural studies, and privacy-preserving anxiety detection approaches.