Züri Fäscht 2013
Open Position »»
Thanks to technological advances, sensors are now embedded in objects, in our environments, and even in our clothing. They are available in ever larger quantities, and, in a few years, sensing will be pervasive.
This transformation from formerly sensor-poor environments into sensor-rich environments changes the paradigm for activity recognition. Rather than thinking about which sensors to deploy for a given recognition task, the question is now how to best make use of available resources.
Thus, we investigate:
Activity recognition in daily life is characterized by an open-ended environment where the set of relevant activities may not be known at
In Smart-days we devise activity recognition running on smartphones which are able to learn and expand their repertoire of known activities at run-time in the most autonomous manner. We achive this by combining:
Methodologies are missing to design context-aware systems that
This limits the real-world deployment of AmI systems.
We develop opportunistic systems that recognize complex
activities/contexts despite the absence of static assumptions about
sensor availability and characteristics.
Nowadays almost every person has a cellphone equipped with sensors. Sensing on such a massive scales opens up avenues for new applications.
Understanding human collective behavior is a challenging multidisciplinary problem with a large number of applications: assistance in emergency and disaster scenarios, urban space planning, as well as smart-traffic management.
Motivated by these possibilities, we investigate how to recognize human collective behaviors from sensors currently included in mobile phones, as well as additional modalities available in our research platforms. We also consider the new possibilities to provide smart assistance based on human collective behavior sensing, e.g. in an emergency situation.
focus on Body Area Networks which include small sensors worn on the body
and found in the immediate environment of the user. These networks
typically have a highly dynamic topology of heterogeneous network nodes,
and include mobile devices such as cell phones or PDAs as well as
resource constrained sensor motes with limited battery power and
The main challenges lie in discovering devices in the environment, organizing distributed processing, and designing adaptable context recognition algorithms.
Diese Website wird in älteren Versionen von Netscape ohne graphische Elemente dargestellt. Die Funktionalität der Website ist aber trotzdem gewährleistet. Wenn Sie diese Website regelmässig benutzen, empfehlen wir Ihnen, auf Ihrem Computer einen aktuellen Browser zu installieren. Weitere Informationen finden Sie auf
The content in this site is accessible to any browser or Internet device, however, some graphics will display correctly only in the newer versions of Netscape. To get the most out of our site we suggest you upgrade to a newer browser.