Smart environments are now designed as natural interfaces to capture and understand human behavior without a need for explicit human-computer interaction. In this article, we present a general-purpose architecture that acquires and understands human behaviors through a sensing floor. The pressure field generated by moving people is captured and analyzed. Specific actions and events are then detected by a low-level processing engine and sent to high-level interfaces providing different functions. The proposed architecture and sensors are modular, general-purpose, cheap, and suitable for both small- and large-area coverage. Some sample entertainment and virtual reality applications that we developed to test the platform are presented.

Surveillance systems can really benefit from the integration of multiple and heterogeneous sensors. In this paper we describe an innovative sensing floor. Thanks to its low cost and ease of installation, the floor is suitable for both private and public environments, from narrow zones to wide areas. The floor is made adding a sensing layer below commercial floating tiles. The sensor is scalable, reliable, and completely invisible to the users. The temporal and spatial resolutions of the data are high enough to identify the presence of people, to recognize their behavior and to detect events in a privacy compliant way. Experimental results on a real prototype implementation confirm the potentiality of the framework.

In this paper we describe TANGerINE, a tangible tabletop environment in which users can interact with digital contents manipulating tangible smart objects. Such objects provide continuous data about their status through the embedded wireless sensors, while an overhead computer vision module tracks their position and orientation. Merging sensing data, the system is able to detect a richer language of gestures and manipulations both on the tabletop and in its surroundings, enabling for a more expressive interaction language across different contexts.

Body Area Sensor Networks (BASN) are an emerging technology enabling the design of natural Human Computer Interfaces (HCI) in the context of Ambient Intelligence. This class of interactive applications poses new challenges on sensor network design that are hard to be faced using traditional solutions optimized for environmental monitoringlike applications. In this paper we present a novel solution for wireless and wearable posture recognition based on a custom-designed wireless body area sensor network, called WiMoCA. Nodes of the network, mounted on different parts of the human body, exploit tri-axial accelerometers to detect body postures. Afterwards we discuss results of interactive performance and power consumption optimizations required to match application constraints.