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Activity recognition using temporal evidence theory

Fri, 09/10/2010 - 13:52 — lorcan

Publication Type:

Journal Article

Authors:

Susan McKeever; Juan Ye; Lorcan Coyle; Chris Bleakley; Simon Dobson

Source:

Journal of Ambient Intelligence and Smart Environments, IOS Press, Volume 2, Issue 3, p.253-269 (2010)

Keywords:

context; context awareness; pervasive computing; ubiquitous computing; dempster-shafer; uncertainty; situation

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.

On using temporal features to create more accurate human-activity classifiers

Thu, 09/24/2009 - 11:05 — lorcan

Publication Type:

Conference Paper

Authors:

Juan Ye; Adrian K. Clear; Lorcan Coyle; Simon Dobson

Source:

20th Conference on Artificial Intelligence and Cognitive Science, UCD Dublin, Ireland, p.274-283 (2009)

Keywords:

context; context awareness; pervasive computing; ubiquitous computing; PlaceLab; sensor networks

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.

A Context Quality Model to Support Transparent Reasoning with Uncertain Context

Wed, 04/29/2009 - 20:31 — lorcan

Publication Type:

Conference Paper

Authors:

Susan McKeever; Juan Ye; Lorcan Coyle; Simon Dobson

Source:

1st International Workshop on Quality of Context (QuaCon), Stuttgart, Germany (2009)

Keywords:

context; context awareness; quality; pervasive computing; ubiquitous computing; uncertainty

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.

Gathering Datasets for Activity Identification

Wed, 01/07/2009 - 09:35 — lorcan

Publication Type:

Conference Paper

Authors:

Lorcan Coyle; Juan Ye; Susan McKeever; Stephen Knox; Matthew Stabeler; Simon Dobson; Paddy Nixon

Source:

Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research. Workshop at CHI’09, Boston, USA (2009)

Keywords:

datasets; pervasive computing; ubiquitous computing; context; activity identification

Abstract:

The area of activity identification is maturing well in the HCI and ubiquitous computing fields. However, although algorithm development is proceedings well, without publicly available datasets on which to compare results it is difficult to consolidate the disparate work being done. This problem exists because realistic datasets describing human activity are difficult and expensive to gather and because there are significant barriers to releasing the data once gathered. We review positive recent development with the release of two high-quality datasets. From our experiences using these datasets we list some recommendations for the gathering and release of future datasets. Finally, we propose a strategy of our own for gathering a new dataset from these recommendations.

Using Situation Lattices in Sensor Analysis

Wed, 01/07/2009 - 09:19 — lorcan

Publication Type:

Conference Paper

Authors:

Juan Ye; Lorcan Coyle; Simon Dobson; Paddy Nixon

Source:

Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom 2009), Texas, USA, p.1-11 (2009)

Keywords:

situation; situation lattice; lattice theory; context; uncertainty

Abstract:

Highly sensorised systems present two parallel challenges: how to design a sensor suite that can efficiently and cost-effectively support the needs of given services; and to extract the semantically relevant interpretations, or "situations", from the flood of context data collected by the sensors. We describe mathematical structures called situation lattices that can be used to address these two problems simultaneously, allowing designers to both design and refine situation identification whilst offering insights into the design of sensor suites. We validate the accuracy and efficiency of our technique against a third-party data set and demonstrate how it can be used to evaluate sensor suite designs.

Scatterbox: Context-Aware Message Management

Tue, 11/04/2008 - 15:47 — lorcan

Publication Type:

Journal Article

Authors:

Stephen Knox; Ross Shannon; Lorcan Coyle; Adrian Clear; Simon Dobson; Aaron Quigley; Paddy Nixon

Source:

Revue d'Intelligence Artificielle, Volume 22, Issue 5, p.549-568 (2008)

Keywords:

context; scatterbox; pervasive computing; ubiquitous computing; email

Abstract:

Applications that rely on mobile devices for user interaction must be mindful of 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. We demonstrate our approach using Scatterbox, a pervasive computing application we have developed which performs sensor fusion to derive a user's current situation. Based on the user's level of interruptibility, Scatterbox prioritises and forwards relevant messages to their mobile phone. We draw conclusions from a preliminary evaluation of the system.

Representing and Manipulating Situation Hierarchies using Situation Lattices

Mon, 09/29/2008 - 11:29 — lorcan

Publication Type:

Journal Article

Authors:

Juan Ye; Lorcan Coyle; Simon Dobson; Paddy Nixon

Source:

Revue d'Intelligence Artificielle, Volume 22, Issue 5, p.647-667 (2008)

Keywords:

situation; situation lattice; lattice theory; context; uncertainty

Abstract:

Situations, the semantic interpretations of context, provide a better basis for selecting adaptive behaviours than context itself. The definition of situations typically rests on the ability to define logical expressions and inference methods to identify particular situations. In this paper we extend this approach to provide for efficient organisation and selection in systems with large numbers of situations having structured relationships to each other. We apply lattice theory to define a specialisation relationship across situations, and show how this can be used to improve the identification of situations using lattice operators and uncertain reasoning. We demonstrate the technique against a real-world dataset.

Resolving Uncertainty in Context Integration and Abstraction

Wed, 09/17/2008 - 17:07 — lorcan

Publication Type:

Conference Paper

Authors:

Juan Ye; Susan McKeever; Lorcan Coyle; Steve Neely; Simon Dobson

Source:

ICPS '08: Proceedings of the 5th international conference on Pervasive services , ACM, Sorrento, Italy, p.131-140 (2008)

Keywords:

bayes networks; uncertainty; context; context awareness

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.

Integrating Multiple Contexts and Ontologies in a Pervasive Computing Framework

Sun, 03/09/2008 - 12:18 — lorcan

Publication Type:

Conference Paper

Authors:

Adrian K. Clear; Stephen Knox; Juan Ye; Lorcan Coyle; Simon Dobson; Paddy Nixon

Source:

Contexts and Ontologies: Theory, Practice and Applications, CEUR Workshop Proceedings, Volume 210, Riva Del Garda, Italy, p.20-25 (2006)

Keywords:

context; ontology; construct; pervasive computing; ubiquitous computing

Abstract:

There is a commonly accepted need for contexts and ontologies to describe the vast amounts of data that are available to pervasive computing applications. Existing contexts and ontologies are either much generalised, very application specific, or inflexible. An integrated approach is required in which new concepts can be added and related to existing ones transparently. This paper describes a novel approach to the design of a set of contexts and ontologies for context-aware pervasive computing systems. It describes a Query Service, that lies between applications and contextual information, which complemented by the contexts and ontologies, offers a more powerful query answering service to application developers than is currently available.

Supplementing Case-based Recommenders with Context Data

Sun, 03/09/2008 - 12:18 — lorcan

Publication Type:

Conference Paper

Authors:

Lorcan Coyle; Evelyn Balfe; Graeme Stevenson; Steve Neely; Simon Dobson; Paddy Nixon; Barry Smyth

Source:

Proceedings of the 1st Workshop on Case-Based Reasoning and Context Awareness, CEUR Workshop Proceedings, Ölüdeniz/Fethiye, Turkey (2006)

Keywords:

case based reasoning; context; context awareness; ticketyboo

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.

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