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- PublicationImplicit interaction : a prerequisite for practical AmIIntelligent User Interfaces represent one of the three distinguishing characteristics of AmI environments. Such interfaces are envisaged as mediating between the services available in an arbitrary physical environment and its inhabitants. To be effective, such interfaces must operate in both proactive and passive contexts, implicitly and explicitly anticipating and responding to user requests. In either case, an awareness of the prevailing situation is essential – a process that demands a judicious combination of data and decision fusion, as well as collaborative and centralized decision making. Given the constraints of AmI environments realizing a distributed lightweight computational infrastructure augmented with a need to address user needs in a timely manner poses significant challenges. In this paper, various issues essential to enabling seamless, intuitive and instinctive interaction in AmI environments are explored.
- PublicationPI : perceiver and interpreter of smart home datasetsPervasive healthcare systems facilitate various aspects of research including sensor technology, software technology, artificial intelligence and human-computer interaction. Researchers can often benefit from access to real-world data sets against which to evaluate new approaches and algorithms. Whilst more than a dozen data sets are currently publicly available, their use of heterogeneous mark-up impedes easy and widespread use. We describe PI – the Perceiver and semantic Interpreter – which offers a workbench API for the querying, re-structuring and re-purposing of a range of diverse data formats currently in use. The use of a single API reduces cognitive overload, improves access, and supports integration of generic and domain-specific information within a common framework.
- PublicationUsing situation lattices in sensor analysisHighly 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.
Scopus© Citations 16 703
- PublicationActivity recognition using temporal evidence theoryThe 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. People's activities in the home often have an identifiable routine; activities 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.
Scopus© Citations 76 1221