Lorcan Coyle's Home on Tinterweb

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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.

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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.

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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.

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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.

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Context-aware systems typically use data sensed from the environment to drive adaptive behaviour. Sensed data is inherently imprecise and uncertain; in addition, new uncertainties are introduced when sensed data is fused with other data to infer context at the more abstract level of situations. We present an uncertainty model which aggregates context uncertainty and provides mechanisms to capture uncertainty at situation level. We demonstrate the application of the model in a sample scenario using an experimental data set.

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With the general increase of life span that our advances in health care have afforded us, more people are suffering from short term memory loss than ever before. Short term memory sufferers often forget what they were doing in the middle of a task and can find themselves in dangerous situations, such as leaving the stove on and leaving the house. They could benefit from an RFID based reminder system that would determine what they were doing based on what objects they touch. To use the system, the user wears an RFID glove which has a reader in the palm. The RFID glove reads the tags on the nearby objects. Along with the RFID glove we are developing an application that enables the user to interact with a reminder application. The application alerts the user of important activities they may have forgotten they started and when an activity is interrupted. It also keeps a record of the list of activities they have performed and objects they have touched through out the day.

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Location-aware systems provide customised services or applications according to users' locations. While much research has been carried out in developing models to represent location information and spatial relationships, it is usually limited to modelling simple environments (cf. [13,19,3]). This paper proposes a unified space model for more complex environments (e.g., city plan or forest). This space model provides a flexible, expressive, and powerful spatial representation. It also proposes a new data structure - an integrated lattice and graph model - to express comprehensive spatial relationships. This structure not only provides multiple graphs at different abstraction levels, but it also collapses the whole map into smaller local graphs. This mechanism is beneficial in reducing the complexity of creating and maintaining a map and improving the efficiency of path finding algorithms.

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Pervasive computing is by its nature open and extensible, and must integrate information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognised within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply ontological techniques successfully to next-generation pervasive systems.

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The concept of calm technology, introduced by Mark Weiser, has led researchers from several disciplines to explore new and unconventional ways of conveying information. Some well-known examples of such novel information techniques include Ambient Devices’ Stock Orb, Koert van Mensvoort's Datafountain, Violet's Nabaztag, Jafarinami et al.'s Breakaway, Mynatt et al.'s Audio Aura and Digital Family Portrait, Mankoff et al.'s Daylight Display and BusMobile, and Natalie Jeremijenko's Dangling String. Applications such as these, that publish information in a non-intrusive or calm manner are Ambient Information Systems.

The 1st International Workshop on the Design and Evaluation of Ambient Information Systems was held in Toronto, Canada, on May 13th 2007, in conjunction with the 5th International Conference on Pervasive Computing. The goal of this workshop was to bring researchers together to discuss this domain of growing interest in both pervasive computing and human-computer interaction. This domain is described by mechanisms that are minimally attended and perceivable from outside the range of a person's direct attention, providing pre-attentive processing without being overly distracting. Developing new technologies such as these poses new and difficult challenges. These technologies display information outside of a person's direct attention, which is a space that is not currently well understood, making it difficult to evaluate their effectiveness. A great deal of care is required to design studies which accurately observe the effect of ambient devices, particularly since the test subjects are not meant to observe them directly. After all, how does one provide a subject with a device and say, "I want you to use this, but please do not think about it?" Our workshop sought to gather perspectives from researchers in the field on these and other problems. Eleven excellent submissions were accepted which describe works-in-progress, frameworks, taxonomies, methodologies, evaluation paradigms, and case studies, and are published here.

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