DARai: Daily Activity Recordings for AI and ML applications

The DARai dataset is a comprehensive multimodal multi-view collection designed to capture daily activities in diverse indoor environments. This dataset incorporates 20 heterogeneous modalities, including environmental sensors, biomechanical measures, and physiological signals, providing a detailed view of human interactions with their surroundings. The recorded activities cover a wide range, such as office work, household chores, personal care, and leisure activities, all set within realistic contexts. The structure of DARai features a hierarchical annotation scheme that classifies human activities into multiple levels: activities, actions, procedures, and interactions. This taxonomy aims to facilitate a thorough understanding of human behavior by capturing the variations and patterns inherent in everyday activities at different levels of granularity. DARai is intended to support research in fields such as activity recognition, contextual computing, health monitoring, and human-machine collaboration. By providing detailed, contextual, and diverse data, this dataset aims to enhance the understanding of human behavior in naturalistic settings and contribute to the development of smart technology applications.