Augmenting the field experience: a student-led comparison of techniques and technologies

In this study we report on our experiences of creating and running a student fieldtrip exercise which allowed students to compare a range of approaches to the design of technologies for augmenting landscape scenes. The main study site is around Keswick in the English Lake District, Cumbria, UK, an attractive upland environment popular with tourists and walkers. The aim of the exercise for the students was to assess the effectiveness of various forms of geographic information in augmenting real landscape scenes, as mediated through a range of techniques and technologies. These techniques were: computer-generated acetate overlays showing annotated wireframe views from certain key points; a custom-designed application running on a PDA; a mediascape running on the mScape software on a GPS-enabled mobile phone; Google Earth on a tablet PC; and a head-mounted in-field Virtual Reality system. Each group of students had all five techniques available to them, and were tasked with comparing them in the context of creating a visitor guide to the area centred on the field centre. Here we summarise their findings and reflect upon some of the broader research questions emerging from the project

Predicting human behavior in smart environments: theory and application to gaze prediction

Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors