Nowadays, activity recognition has been proposed in several researches. It is attractive to improve the ability of the activity recognition system because existing research on activity recognition systems still have an error in an ambiguous cases. In this paper, we introduce the novel technique to improve the activity recognition system in smart home domain. We propose the three contributions in this research. Firstly, we design the context-aware infrastructure ontology for modelling the user's context in the smart home. The innovative data, human posture, is added into the user's context for reducing the ambiguous cases. Secondly, we propose the concepts to distinguish the activities by object-based and location-based concepts. We also present the description logic (DL) rules for making the human activity decision based on our proposed concepts. Lastly, We conduct the Ontology Based Activity Recognition (OBAR) system for two purposes: to recognize the human activity, and to search the semantic information in the system, called semantic ontology search (SOS) system. The results show the system can recognize the human activity correctly and also reduce the ambiguous case.Index Terms-Activity recognition, Context-aware infrastructure ontology, Ontology based activity recognition, Semantic ontology search 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems 978-0-7695-4861-6/12 $26.00

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Although the Semantic Web data standards are established, ontology-based applications built on the standards are relatively limited. This is partly due to high learning curve and efforts demanded in building ontology-based Semantic Web applications. In this paper, we describe an ontology application management (OAM) framework that aims to simplify creation and adoption of ontology-based application that is based on the Semantic Web technology. OAM introduces an intermediate layer between user application and programming and development environment in order to support ontology-based data publishing and access, abstraction and interoperability. The framework focuses on providing reusable and configurable data and application templates, which allow the users to create the applications without programming skill required. Three forms of templates are introduced: database to ontology mapping configuration, recommendation rule and application templates. We describe two case studies that adopted the framework: activity recognition in smart home domain and thalassemia clinical support system, and how the framework was used in simplifying development in both projects. In addition, we provide some performance evaluation results to show that, by limiting expressiveness of the rule language, a specialized form of recommendation processor can be developed for more efficient performance. Some advantages and limitations of the application framework in ontology-based applications are also discussed.

With a lifetime prevalence of 2.4%, 1.5 million Thai people suffer from depression in their lifetime. Depression is the leading cause of disability and a major contributor to the overall burden of disease. With the lack of response to this health, the challenge has been compounded by negative perception of mental illness, less than half of depression individuals in Thailand access mental health services. This results in increased number of depression by about 18%. More proactive service should be considered. The sooner depression is detected, the less complicated and shorter course of therapy it would be. This research applies Natural Language Processing (NLP) techniques in psychological domains to develop a depression detection algorithm for the Thai language. Since Facebook is the most popular social network in Thailand. It is often used for sharing opinions and life events. This research proposes Facebook utilization as a large-scale resource to develop depression screening model. The features for depression detection in the Thai community are recognized.

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