A hybrid model for capturing implicit spatial knowledge

This paper proposes a machine learning-based approach for capturing rules embedded in users’ movement paths while navigating in Virtual Environments (VEs). It is argued that this methodology and the set of navigational rules which it provides should be regarded as a starting point for designing adaptive VEs able to provide navigation support. This is a major contribution of this work, given that the up-to-date adaptivity for navigable VEs has been primarily delivered through the manipulation of navigational cues with little reference to the user model of navigation

Individual differences in navigating and experiencing presence in virtual environments

The effort of making Virtual Environments (VEs) more useful and satisfactory to use lie at the core of usability research. Because of their development and widespread accessibility, VEs are being used by an ever-increasing diversity of users, whose individual differences impact on both task performance and level of satisfaction. This aspect raises a major challenge in terms of designing adaptive VEs, suitable not for the average user but for each individual user. One way to address this challenge is through the study of individual differences and their implications, which should lead to new effective ways to accommodate them. Adaptivity reflects the system’s capability to automatically tailor itself to dynamically changing user behaviour. This capability is enabled by a user model, acquired on the basis of identifying the user’s patterns of behaviour. This thesis addresses the issue of studying and accommodating individual differences with the purpose of designing adaptive VEs. The individual differences chosen to be investigated are those that impact particularly on two fundamental aspects underlying each interaction with a VE, namely navigation and sense of presence. Both these aspects are related to the perceived usability of VEs. The impact that a set of factors like empathy, absorption, creative imagination and willingness to be transported within the virtual world has on presence has been investigated and described through a prediction equation. Based on these findings, a set of guidelines has been developed for designing VEs able to accommodate these individual differences in order to support users to experience a higher level of presence. The individual differences related to navigation within VE have been investigated in the light of discriminating between efficient versus inefficient search strategies. Building a user model of navigation affords not only a better understanding of user spatial behaviour, but also supports the development of an adaptive VE which could help low spatial users to improve their navigational skills by teaching them the efficient navigational rules and strategies

Trajectory analysis using point distribution models:algorithms, performance evaluation, and experimental validation using mobile robots

This thesis focuses on the analysis of the trajectories of a mobile agent. It presents different techniques to acquire a quantitative measure of the difference between two trajectories or two trajectory datasets. A novel approach is presented here, based on the Point Distribution Model (PDM). This model was developed by computer vision scientists to compare deformable shapes. This thesis presents the mathematical reformulation of the PDM to fit spatiotemporal data, such as trajectory information. The behavior of a mobile agent can rarely be represented by a unique trajectory, as its stochastic component will not be taken into account. Thus, the PDM focuses on the comparison of trajectory datasets. If the difference between datasets is greater than the variation within each dataset, it will be observable in the first few dimensions of the PDM. Moreover, this difference can also be quantified using the inter-cluster distance defined in this thesis. The resulting measure is much more efficient than visual comparisons of trajectories, as are often made in existing scientific literature. This thesis also compares the PDM with standard techniques, such as statistical tests, Hidden Markov Models (HMMs) or Correlated Random Walk (CRW) models. As a PDM is a linear transformation of space, it is much simpler to comprehend. Moreover, spatial representations of the deformation modes can easily be constructed in order to make the model more intuitive. This thesis also presents the limits of the PDM and offers other solutions when it is not adequate. From the different results obtained, it can be pointed out that no universal solution exists for the analysis of trajectories, however, solutions were found and described for all of the problems presented in this thesis. As the PDM requires that all the trajectories consist of the same number of points, techniques of resampling were studied. The main solution was developed for trajectories generated on a track, such as the trajectory of a car on a road or the trajectory of a pedestrian in a hallway. The different resampling techniques presented in this thesis provide solutions to all the experimental setups studied, and can easily be modified to fit other scenarios. It is however very important to understand how they work and to tune their parameters according to the characteristics of the experimental setup. The main principle of this thesis is that analysis techniques and data representations must be appropriately selected with respect to the fundamental goal. Even a simple tool such as the t-test can occasionally be sufficient to measure trajectory differences. However, if no dissimilarity can be observed, it does not necessarily mean that the trajectories are equal – it merely indicates that the analyzed feature is similar. Alternatively, other more complex methods could be used to highlight differences. Ultimately, two trajectories are equal if and only if they consist of the exact same sequence of points. Otherwise, a difference can always be found. Thus, it is important to know which trajectory features have to be compared. Finally, the diverse techniques used in this thesis offer a complete methodology to analyze trajectories

User Model of Navigation

This study proposes a user model of navigation in a Virtual Environment (VE), based on investigating the differences in movement patterns. Two methodologies enable accessing navigational rules and strategies employed by different groups of users: high versus low spatial users. These captured rules are summarised and hierarchically organised in a coherent structure which constitutes a basis for an efficient model of navigation. Implications of this model for designing navigable VEs are discussed