situation

Activity recognition using temporal evidence theory

Publication Type:

Journal Article

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.

Using Ontologies in Case-Based Activity Recognition

Publication Type:

Conference Paper

Source:

23rd Florida Artificial Intelligence Research Society Conference (FLAIRS-23), AAAI (2010)

Keywords:

case based reasoning; ontology; pervasive computing; ubiquitous computing; situation; context awareness

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.

Using Situation Lattices in Sensor Analysis

Publication Type:

Conference Paper

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.

Representing and Manipulating Situation Hierarchies using Situation Lattices

Publication Type:

Journal Article

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.

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