In order to understand our physical condition, we need to record the detail of physical condition data like the heart rate. However, for understanding such data, additional information such as what the subject is doing at that time is needed. We propose a combined system which consists of a fuzzified neural network based unusual condition detection and a standard neural network based action estimation. From experimental results, the effectiveness of our proposed system is shown for understanding our conditions.

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Improving the quality of nursing care is crucial to maintaining the quality of life. Our objective is to develop a computer-aided evaluation system that enables nursing experts to improve the quality of nursing care. In our previous works, some classification systems based on fuzzy logic, neural networks, and SVMs were developed. Although a classification system with high performance for all nursing-care datasets is desirable, we focus on how to visualize the classification results in this paper. It is important to visualize the results for our nursing-care text classification system because the computer-aided system has to explain the reasons for obtaining such results to human experts. In this paper, a tree-type expression is considered for visualizing the classification results. To visualize classification results with the tree-type expression, we consider a decision tree technique. Word existence, dependency relations, and phrase-based feature vector definitions have been proposed in our previous works. In the present study, these three types of feature vector definitions are compared with one another from the viewpoint of understandability.

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