Sensing and Interactive Intelligence in Mobile Context Aware Systems

Abstract

The ever increasing capabilities of mobile devices such as smartphones and their ubiquityin daily life has resulted in a large and interesting body of research into contextawareness { the `awareness of a situation' { and how it could make people's lives easier.There are, however, diculties involved in realising and implementing context awaresystems in the real world; particularly in a mobile environment.To address these diculties, this dissertation tackles the broad problem of designing andimplementing mobile context aware systems in the eld. Spanning the elds of ArticialIntelligence (AI) and Human Computer Interaction (HCI), the problem is broken downand scoped into two key areas: context sensing and interactive intelligence. Using asimple design model, the dissertation makes a series of contributions within each areain 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 showthat 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 bymobile 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 thecase of semantic place awareness to address the problems of real time context inferenceand learning on mobile devices. We show that raw device motion { a commonmetric used in activity recognition research { is a poor indicator of transition betweensemantically meaningful places, but real time transition detection performance can beimproved with the application of basic machine learning and time series processingtechniques. We also develop a context inference and learning algorithm that incorporatesuser feedback into the inference process { a form of active machine learning. Wecompare various implementations of the algorithm for the semantic place awarenessuse case, and observe its performance using a simulation study of user feedback.For the interaction component, we study various approaches for eliciting user feedbackin the eld. We deploy the mobile semantic place awareness system in the eld andshow how dierent elicitation approaches aect user feedback behaviour. Moreover,we report on the user experience of interacting with the intelligent system and showhow performance in the eld compares with the earlier simulation. We also analyse theresource usage of the system and report on the use of a simple SMS place awarenessapplication that uses our system.The dissertation presents original research on key components for designing and implementingmobile context aware systems, and contributes new knowledge to the eld ofmobile context awareness.

Date of Award	31 Dec 2013
Original language	English
Awarding Institution	University of Bath
Supervisor	Eamonn O'Neill (Supervisor)