Contextual experience prototyping.

Creating prototypes for interactive systems is an essential part of today's research areas of human computer interaction (HCI) as well as pervasive and ubiquitous computing. Recently the focus of prototypes shifts from pure functional or design prototypes to experience prototypes to explore parameters and factors that are essential for developing and designing good and appropriate User Experiences. In this tutorial we first explore new and enhanced approaches towards the application of methods and tools for prototyping experiences. The participants will gain insights in methods like the Wizard of Oz methodology applied for experience prototyping purposes.
Areas like contextual prototyping will be further elaborated towards the prototyping of experiences with the goal to study such experiences in the lab but also in the wild. After exploring the area of experience prototyping the participants will take part in an interactive session.
We will provide an introduction into the Contextual Interaction Framework - CIF, a modular approach to prototype interactive systems as well as to perform studies and to immediately collect relevant data.
Different prototyping approaches like prototyping with sensors, mobile devices, as well as wizard of Oz prototyping with the CIF will be elaborated in an interactive way with the participants. The goal of the tutorial is to provide the participants with an idea about how to adress experiences already during the prototyping phase. Hands-on examples will be elaborated and analyzed towards the appropriateness of the prototypes for studying user experiences in various contexts.

Activity recognition with a focus on collective sensing and
crowd sourced data.

The tutorial will discuss techniques for extracting complex activity  information from a broad range of ubiquitous and body worn sensors.  While a summary will be given of basic recognition techniques and sensing modalities  the focus will be on emerging topics including:

·         Dealing with dynamic  configurations of information sources (opportunistic recognition). This will include methods for compensating changes in sensor properties (e.g. the on body location in which a smart phone is carried), the disappearance of sensors and the appearance of new sources of information

·         Crowd sourcing sensor data and collaborative sensing. Today in many environments nearly everyone has one or more  sensor enabled consumer device.  Thus, instead of deploying dedicated sensors there is an increasing trend towards leveraging such devices and collecting data from volunteers. However in many cases streaming raw data is not an option (due to provacyy and bandwidth issues). Instead the information needs to be aggregated on the devices and in peer-2-peer collaboration between devices. 

·         Recognition of collective and social phenomena. When collecting and evaluating data from many users and systems in their environment we can obtain information that concerns not just individual activities but also behaviors and  states related to groups of people. A simple example is the aggregation of location and motion data from individual users to infer crowd behavior during large public gatherings.