Lorcan Coyle's Home on Tinterweb

Keywords:

Abstract:

The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. Peoples' activities in the home often have an identifiable routine; activities take place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Pervasive computing requires the ability to detect user activity in order to provide situation-specific services. Case-based reasoning can be used for activity recognition by using sensor data obtained from the environment. Pervasive computing systems can grow to be very large, containing many users, sensors, objects and situations, thus raising the issue of scalability. This paper presents a case-based reasoning approach to activity recognition in a smart home setting. An analysis is performed on scalability with respect to case storage, and an ontology-based approach is proposed for case base maintenance. We succeeded in reducing the casebase size by a factor of one thousand, while increasing the accuracy in recognising some activities.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being 'context-aware'. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enabling the inference process. In our work, we apply the Dempster Shafer theory of evidence to infer situation occurrence with minimal use of training data. We describe a set of evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home activity data set.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Through advances in sensing technology, a huge amount of data is available to context-aware applications. A major challenge is extracting features of this data that correlate to high-level human activities. Time, while being semantically rich and an essentially free source of information, has not received sufficient attention for this task. In this paper, we examine the potential for taking temporal features—inherent in human activities—into account when classifying them. Preliminary experiments using the PlaceLab dataset show that absolute time and temporal relationships between activities can improve the accuracy of activity classifiers.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Much research on context quality in context-aware systems divides into two strands: (1) the qualitative identification of quality measures and (2) the use of uncertain reasoning techniques. In this paper, we combine these two strands, exploring the problem of how to identify and propagate quality through the different context layers in order to support the context reasoning process. We present a generalised, structured context quality model that supports aggregation of quality from sensor up to situation level. Our model supports reasoning processes that explicitly aggregate context quality, by enabling the identification and quantification of appropriate quality parameters. We demonstrate the efficacy of our model using an experimental sensor data set, gaining a significant improvement in situation recognition for our voting based reasoning algorithm.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Pervasive computing is typically highly sensor-driven, but sensors provide only evidence of fact rather than facts themselves. The uncertainty of sensor data will affect each component in a pervasive computing system, which may decrease the quality of its provided services. We provide a general model to represent semantics of uncertainty in different levels (e.g., sensor, lower-level context and higher-level context). Within our model, fine-grained approaches are applied to evaluate and propagate uncertainties. They will help to resolve the uncertainty in each process of context management so that the effect of uncertainty on system services will be minimised.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

As e-commerce has become more popular, the problem of information overload has come to the fore. Recommender systems that reduce the information overload problem are becoming more common. However, the problem with many recommender systems is that they are associated with a high cost of learning customer preferences (in terms of cognitive load). We describe the Personal Travel Assistant (PTA), a flight recommender application that uses case-based reasoning (CBR) to overcome these problems.

The PTA allows users to search multiple flights providers concurrently and recommends flights based on their individual travel preferences. These preferences are implicitly learned from observations of user behaviour. When the user purchases a flight, the PTA uses the selection of a preferred flight to discover and refine the user's overall travel preferences. These preferences are stored in a user-model as sets of cases representing their interactions, which are used to provide personalised recommendations.

The PTA makes recommendations taking into account the context in which the flights were offered. It uses features from the request to determine this context, e.g. the duration of the trip. We perform evaluations of contextual recommendations that support our view that user preferences change depending on the context of the session. We further improve recommendation accuracy by storing and personalising similarity measures in the user-model. The PTA alters the relative importance of features in the personal similarity measure based on implicit user feedback, e.g. increasing the importance of price at the cost of stop-over time in a multiple hop flight.

We also investigate cooperative components to extend our recommendation strategies. These allow users to reuse the information learned from other users when they encounter new situations. However, these techniques are not as successful as we had hoped. We discuss these components in relation to other work on collaborative recommendation and suggest that the standard approach is unsuited to the PTA's context-based recommendation strategy.

The strength of CBR in the e-commerce domain stems from its reuse of the knowledge base associated with a particular application. Since case data may be one aspect of a company's entire knowledge system, it is important to integrate case data easily within a company's IT infrastructure, providing in effect a case-based view on relevant portions of the company knowledge base. We describe CBML, an XML-based Case Mark-Up Language we have developed to facilitate such integration.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

We propose that traditional case-based recommender systems can be improved by informing them with context data describing the user's environment. We outline existing applications with similar objectives and describe an application of our own - Ticketyboo - which uses music listening preferences and context information from users' calendars to recommend tickets for music concerts. This data is gathered by virtual sensors that monitor each user's music player and calendar applications. The novelty of this approach is that context data is provided to Ticketyboo via a dedicated context infrastructure. This results in a clear separation between the providers and consumers of context data. By utilising context data in this way, minimal user input/feedback is required to guide the system since the need for explicit user feedback is negated.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Context-aware systems that rely on mobile devices for user interaction must address the low bandwidth of both communications and more importantly the user's limited attention, which will typically be split between several competing tasks. Content delivery in such systems must be adapted closely to users' evolving situations and shifting priorities, in a way that cannot be accomplished using static filtering determined a priori. We propose a more dynamic context-driven approach to content delivery, that integrates information from a wide range of sources. We demonstrate our approach on a system for adaptive message prioritisation and forwarding.

• Tagged
• XML
• BibTex

Keywords:

Abstract:

Much recent research has focused on using situations rather than individual pieces of context as a means to trigger adaptive system behaviour. While current research on situations emphasises their representation and composition, they do not provide an approach on how to organise and identify their occurrences efficiently. This paper describes how lattice theory can be utilised to organise situations, which reflects the internal structure of situations such as generalisation and dependence. We claim that situation lattices will prove beneficial in identifying situations, and maintaining the consistency and integrity of situations. They will also help in resolving the uncertainty issues inherent in context and situations by working with Bayesian Networks.

• Tagged
• XML
• BibTex