Providing affordable, high-quality healthcare to the elderly while enabling them to live independently longer is of critical importance. In our Smart Condo project, we have deployed a wireless sensor network in an 850-square-foot condominium for assisted living. The sensor network records a variety of events and environmental parameters and feeds the related data into our web-based system. This system is responsible for inferring higher-order information about the activities of the condo's occupant and visualizing the collected information in both a 2D Geographic Information System (GIS) and a 3D virtual world. The architecture is flexible in terms of supported sensor types, analyses, and visualizations through which it communicates this information to its users, including the condo's occupant, their family, and their healthcare providers.
Providing affordable, high-quality healthcare to the elderly while enabling them to live independently longer is of critical importance, as this is an increasing and expensive demographic to treat. Sensor-network technologies are essential to developing assisted living environments. In our Smart Condo project, we have deployed a sensor network with a variety of sensor types in an 850 square-foot condominium. The sensor network records a variety of events and environmental parameters and feeds the related data into our web-based system. This system is responsible for inferring higher-order information about the activities of the condo's occupant and supporting the visualization of the collected information in a 2D Geographic Information System (GIS) and a 3D virtual world, namely Second Life (SL).
Becoming a skilled professional requires the acquisition of theoretical knowledge and the practice of skills under the guidance of an expert. The idea of learning-throughapprenticeship is long accepted in medicine and, more generally, in the health sciences, where practicum courses are an essential part of most curricula. Because of the high cost of apprenticeship programs -mentors can usually supervise few trainees and trainees may need long apprenticeship periods -simulation has long been adopted as a learning-by-doing training method that can supplement apprenticeship in many professional and engineering programs, including the health sciences. In this paper, we describe our experience developing virtual world-based training systems for two healthcare contexts. In one, procedural training was emphasized, while the other focused on teaching communication skills. In each case, we developed a custom set of tools to meet the needs of that context. We present an analysis of the case studies, and lessons drawn from this analysis.
Becoming a skilled professional requires both the acquisition of theoretical knowledge and the practice of skills relevant to one’s profession. When learning by doing, students consolidate their knowledge of domain-specific facts by applying them as necessary to accomplish the tasks involved in their profession. Simulation-based learning methods are a family of methods that enable this learning mode. New computer related technologies, including high performance networking, high definition displays, distributed multiplayer game engines, and virtual worlds, bring new opportunities for simulation-based learning methods and systems. In this work, we describe our software framework for specifying simulation-based lesson plans and their implementations on two different platforms: a video based tool and a virtual world environment. We discuss the software architecture of the system, illustrate its functionality with an example lesson on how to conduct oneself in corporate interviews, outline our plans for experimental evaluation, and argue for its usefulness in today’s efforts to creatively use virtual worlds for educational purposes.
The classification of an unknown item based on a training data set is a key data mining task. An important part of this process that is often overlooked is the user's comprehension of the classifier and the results it produces. Associative classifiers begin to address this issue by using sets of simple rules to classify items. However, the size of these rule sets can be an obstacle to understandability. In this work, we present an interactive visualization system that allows the user to visualize various aspects of the classifier's decision process. This system shows the rules that are relevant to the classification of an item, the ways in which the item's characteristics relate to these rules, and connections between the item and the classifier's training data set. The system also contains a speculation component, which allows the user to modify rules within the classifier, and see the impact of these changes. Thus, this component allows the user to contribute domain expertise to the classification process, consequently improving the accuracy of the classifier.
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