4,483 research outputs found

    Context Trees: Augmenting Geospatial Trajectories with Context

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    Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

    Habitualisation: localisation without location data

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    This paper looks at identifying the locations of users from the Nokia MDC dataset throughout the day without taking into consideration location based data. By looking at a users habits and idiosyncrasies we determined the likelihood of a users location within known stay regions which we call habitats. The features used to determine location were extracted from a users interaction with the smart phone. None of the features contained a users locations or a users proximity to objects with known locations. Using a set of structured output support vector learning techniques we found that a users location with respect to the areas of typical activities is well predictable solely from daily routines and a smart phone usage habits

    Location Based Indoor and Outdoor Lightweight Activity Recognition System

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    In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure
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