Understanding human behavior is one of the most intriguing and fascinating research domains, which encompasses several research fields, ranging from economics and sociology to more recently computer science. Immense progress in sensor and communication technologies has led to the development of devices and systems recording daily human activities in both real and virtual (web-based) settings. This has led to an increase of research on the design of algorithms capable of inferring meaningful behavioral patterns of human activities from the information contained in data logs or captured by sensors. Simultaneously, there are new application opportunities in many domains such as surveillance, health care monitoring, social networking, and recommendation systems.
The aim of the HAI project is to investigate the above domain by performing long-term research which addresses fundamental questions and common tasks of this domain: how to design robust features for accurate activity/interaction representation~? How to learn or discover activity patterns, introduce hierarchies or temporal order at different scales, and deal efficiently with large amounts of data~? how to infer contextual information that affects activity patterns or their occurrence, or facilitates their interpretation. To achieve these goals, we will investigate new approaches by anchoring the design of general activity models in the context of three different and relatively recent application domains with specific scenarios, types of activities, and data modalities.