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Title: Sensing and interactive intelligence in mobile context aware systems
Author: Lovett, Tom
ISNI:       0000 0004 2746 426X
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2013
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The ever increasing capabilities of mobile devices such as smartphones and their ubiquity in daily life has resulted in a large and interesting body of research into context awareness { the `awareness of a situation' { and how it could make people's lives easier. There are, however, diculties involved in realising and implementing context aware systems in the real world; particularly in a mobile environment. To address these diculties, this dissertation tackles the broad problem of designing and implementing mobile context aware systems in the eld. Spanning the elds of Articial Intelligence (AI) and Human Computer Interaction (HCI), the problem is broken down and scoped into two key areas: context sensing and interactive intelligence. Using a simple design model, the dissertation makes a series of contributions within each area in order to improve the knowledge of mobile context aware systems engineering. At the sensing level, we review mobile sensing capabilities and use a case study to show that the everyday calendar is a noisy `sensor' of context. We also show that its `signal', i.e. useful context, can be extracted using logical data fusion with context supplied by mobile devices. For interactive intelligence, there are two fundamental components: the intelligence, which is concerned with context inference and machine learning; and the interaction, which is concerned with user interaction. For the intelligence component, we use the case of semantic place awareness to address the problems of real time context inference and learning on mobile devices. We show that raw device motion { a common metric used in activity recognition research { is a poor indicator of transition between semantically meaningful places, but real time transition detection performance can be improved with the application of basic machine learning and time series processing techniques. We also develop a context inference and learning algorithm that incorporates user feedback into the inference process { a form of active machine learning. We compare various implementations of the algorithm for the semantic place awareness use case, and observe its performance using a simulation study of user feedback. For the interaction component, we study various approaches for eliciting user feedback in the eld. We deploy the mobile semantic place awareness system in the eld and show how dierent elicitation approaches aect user feedback behaviour. Moreover, we report on the user experience of interacting with the intelligent system and show how performance in the eld compares with the earlier simulation. We also analyse the resource usage of the system and report on the use of a simple SMS place awareness application that uses our system. The dissertation presents original research on key components for designing and implementing mobile context aware systems, and contributes new knowledge to the eld of mobile context awareness.
Supervisor: O'Neill, Eamonn Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: intelligence ; context awareness ; mobile sensing ; al ; hci