Section: Application Domains
User localization in large-scale perceptive environment using multimodal heterogenic analysis
Ad-hoc assemblies of mobile devices embedding sensing, display, computing, communications, and interaction provide an enabling technology for smart environments. As described above, in the PRIMA project we have adopted a component oriented programming approach to compose smart services for such environments. Common services for smart environement include
Services to manage energy in building, including regulating temperature, illumination, and acoustic noise,
Ambient assisted living services to extend the autonomy of elderly and infirm,
Logistics management for daily living,
Communication services and tools for collaborative work,
Services for commercial environments,
Orientation and information services for public spaces, and
Services for education and training.
We wish to develop the concept of "large-scale" perceptive space that is an intelligent environment which will be deployed on a large surface containing several buildings (as a university campus for example). We also define the "augmented man" concept as a human wearing one or many mobile intelligent wireless devices (telephone, Smartphone, pda, notebook). Using all these devices, one can use many different applications (read emails, browse the Internet, file exchange, etc.). By combining the concepts of large-scale perceptive environments and mobile computing, we can create intelligent spaces, it becomes possible to propose services adapted to individuals and their activities. We are currently focussing on two aspects of this problem: the user profile and the user location within a smart space.
A fundamental requirement for such services is the ability to perceive the current state of the environment. Depending on the nature of the service, environment state can require sensing and modeling the physical properties of the environment, the location, identity and activity of individuals within the environment, as well as the set of available computing devices and software components that compose the environment. All of these make up possible elements for context modeling.
Observing and tracking people in smart environments remains a challenging fundamental problem. Whether it is at the scale of a campus, of a building or more simply of a room, we can combine several additional localization levels (and several technologies) to allow a more accurate and reliable user perception system. Within the PRIMA project, we are currently experimenting with a multi-level localization system allowing variable granularity according to the available equipment and the precision required for the targeted service. This approach is in the same research area as the MagicMap system. However, contrary to MagicMap, we enrich location information from wireless technologies with information from 3D tracking using cameras, microphones, and other sensing devices.