Most would agree: human vision is the most important of the five senses. Tragically many elderly people lose their vision due to incurable diseases which could have been avoided if diagnosed early enough. Fortunately some of these diseases can be diagnosed or at least have their symptoms detected with the use of simple tests. The use of smartphone or tablet applications have become common for these tests, so eye diseases can be detected early and even at home. However, none of the smartphone or tablet applications considers the screen to face distance of a person doing an eye test to be an important parameter for these sorts of tests. In this paper we present an algorithm to derive a new context: the smartphone user's screen to face distance. Our algorithm utilizes the smartphone front camera and an eye detection algorithm. After initializing the algorithm with person specific values, the algorithm continuously measures the eye to eye distance to derive the user's actual screen to face distance. We also present an investigation on the algorithm accuracy and speed, which shows: a smartphone based screen to face distance measurement is possible in the distance range from 19cm to 94cm with a maximum deviation of 2.1cm and at a rate of three distance measurements per second.
Services are becoming the foundation of the Telecommunication and Internet world. The ability to fast and cost-efficient create, provision and manage services will become a driving force in the overall competition. A major step forward for a rich service environment is to give end-users the ability to create their own services. The SPICE platform provides an environment for future converged services. Using the SPICE tool chain for service creation professional developers can build services combining Internet and Telecommunication service enablers. End-user driven service creation explore the creativity and needs of the real users. We explain how professional services can be created in such a way that they stimulate the uptake of end-user services. Finally, we explain the design aspect of privacy preserving service enablers which will allow network operator to take a central role in the overall service economy while preserving essential requirements like privacy.
Context prediction is a key technique for proactive environments adapting to user's needs. To prevent wrong predictions is one key factor to achieve a high user acceptance. A wrong prediction could be caused by faulty or disturbed sensor data. With the triumph of the Smartphone, a wide range of context sources has become ubiquitous. Often, context prediction approaches today do not utilize these multiple context sources to cope with faulty or disturbed sensor data. We propose and evaluate an approach that uses multiple context sources and exploits the correlations between context sources of one user to get a more fault tolerant prediction.
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