A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.
The transportation domain brings particular needs dealing with the specificities of the environment (highly mobile, distributed, unstable network connection, ...). In this paper, we propose a dynamic adaptable framework responding to the needs of transportation's applicative services. Among the most important services we can name positioning, time or communication capabilities. To achieve both this flexibility and automate context adaptation, we rely on a Service Oriented architecture and experiment our proposition on the inter-vehicular communication system, VESPA. This framework is based on the use of context informations for the transportation domain.
In the last decade, a number of wireless and small-sized devices (e.g., PDAs, smartphones, sensors, laptops, etc.) with increasing computing capabilities have appeared in the market at very affordable costs. These devices have started to be embedded in modern cars in the form of on-board computers, GPS navigators, or even multimedia centers. Thus, the vehicles can carry useful information, acting as data sources for other vehicles. Recently, some works have addressed the problem of processing queries in such highly dynamic vehicular networks in order to share information between drivers. The proposed query processing techniques usually rely on a push model. Hence, each vehicle receives data from its neighbors and decides whether they are relevant enough to be stored in a local data cache. Then, the data may be used by a query processor to retrieve relevant data for the driver. In this paper, we look at the problem from a broader perspective and discuss the interest of multi-scale query processing techniques in such context. The goal of such techniques is to exploit, at the mobile device's level, different access modes (e.g., push, pull) and various data sources (e.g., data cached locally, data stored by vehicles nearby, remote Web services, etc.) to provide the users with results for their queries. We highlight the most important challenges and outline some possible approaches. We also present a prototype of a first query evaluator developed using the Microsoft LINQ API.
Smart environments and technology used for elder care, increases independent living time and cuts long-term care costs. A key requirement for these systems consists in detecting and informing about abnormal behavior in users'routines. In this paper, our objective is to automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning method to formalize a normal behavior pattern for each elderly people related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior changes over time. In change behavior period we focus on each activity to detect anomalies. A use case with real datasets are promising.
Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.
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