Summary of our expertise

• Indoor positioning, using all existing infrastructure (Wi-Fi, Bluetooth...)
• Detection of unusual behaviors (spending to much time in the bathroom, not passing by the kitchen at lunch time...)
• Detection of dangers (falling down, stress and panic, not getting up at wake-up time...)

We are currently working on 4 AAL projects (Trainutri, WayFiS, MyGuardian, Virgilius), where we've already acquired a solid expertise in the above mentioned points. In Call 5 we position ourself has technology provider. We want to provide data related to the position and the behavior of the user to a higher-level service, which can then use it in order to offer the best answer to the elderly person or his caregivers. We assume that the end-users (elderly people and caregivers) are wearing a mobile device with them.

Expertise on indoor positioning

Our indoor localization work is centralized in a module called GPM (Global Positioning Module). GPM offers a unified way to get your position via different technologies, like GPS, Bluetooth, Wi-Fi fingerprints or barcodes. Internal sensors like the accelerometer, the gyroscope or the pressure chip are used to interpolate the location in areas where then main position providers cannot work properly. Released as a framework, GPM allows easily the integration of a new positioning system and offers bridges to existing commercial solutions like the ones used in care centers to locate patients and doctors. Each position provider comes with a specification file which allows the module to select automatically the best one according to the current context. The power consumption is closely and controlled so that GPM returns the best ratio between precision and battery usage.

Expertise on detection of unusual behavior

By studying the moves of a user in its home, we can detect unusual behaviors. For instance a user spending too much time in some areas (bathroom, corridor...) or that doesn't pass by some "checkpoints" (kitchen at lunch time, toilets for a while...) can be suspicious. Technically it consists in defining areas in the home and binding to each of them a set of rules. A rule that isn't met indicates a unusual behavior which deserves to be communicated to a caregiver.

Expertise on detection of dangers

While the detection of unusual behavior is GIS-based and rarely an emergency situation, this "detection of dangers" topic consists in analyzing the sensors of the mobile phone in order to be warned of a possible emergency situation. This includes the "classical" falling down that can be detected with the accelerometer, but also some more advanced analysis to detect when the user has an unusual high activity, indicating perhaps a panicking or a stressful situation.

We can provide a full sub-consortium!

Thanks to our currently running AAL projects and therefore our numerous links with industrial partners, we can if needed provide a full academic-SME sub-consortium. In this case, we as university would fully concentrate on the research aspects while our industrial partner would focus his work on the use cases and the industrialization of the product.

Why to choose us?

• Good experience in AAL projects (4 currently running)
• With a budget of 4M, Switzerland is a safe partner
• Good business experience
• Used to work with high quality standards (IS0 9001)
• Dissemination experience (publication and media)
• We can provide a full sub-consortium