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Objectives: A program fostering self-management for the elderly was implemented and the effects of the program and their continuities were assessed. Methods: Subjects consisted of 19 males and 131 females (average age, 73.1 ± 7.4; range, 60–94 years). The intervention program consisted of the collective experience learning and private consultation. The collective experience learnings included; (1) monitoring the oral condition and practicing oral self-care, (2) monitoring the oral function and practicing oral exercises, and (3) group discussion on continuing oral self-care. Outcomes were evaluated at the beginning and the end of the intervention program, and three months after the investigation by the scores in; (1) oral self-care (2) oral condition, i.e., decayed teeth, community periodontal index (CPI), deposits of plaque and dental calculus, (3) oral function such as RSST, oral diadochokinesis, (4) QOL (SF-8 v2™, and GOHAI), and (5) cognitive function (MMSE-J). Informed consent was obtained fro...

In this paper, a convolution neural network (CNN) based text classification method is proposed. CNNs show strong performance for computer vision and speech recognition applications. Recently, in some researches, CNNs have been applied to sentence classification applications. Currently, we have studied nursing-care text classification to improve nursing-care quality in Japan. In our former works, several types of feature definitions have been proposed and examined by some classification models like SVMs. In this paper, a single layer CNN is used for classifying nursing-care texts. Each nursing-care text is represented as a concatenated word vectors. Each word is represented as a fixed length word vector which is obtained by the word2vec. Then, nursing-care texts are classified using a two-dimensional CNN-based classification method. The proposed CNN has a new kind of filters which extracts dependency relation between words. From our experimental results, the proposed CNN-based method obtained better performance than our former works.

In this paper, we propose a convolutional neural network (CNN) based classification method for nursing-care classification. CNNs have obtained strong performance in computer vision speech recognition areas. Recently, CNNs have been also applied sentence classification. We have studied nursing-care text classification [6]-[18]. In our former works, we proposed several types of feature definitions and examined some classification models. In this paper, each text is represented as a concatenated word vector. Then, every text is classified using CNN-based classification methods. We examined some classification models at the classification layer in CNNs. From our experimental results, the proposed CNN-based method obtained better performance than our former works.

It is very important for us to improve the nursing-care quality. To improve the nursing-care quality, the “Web based Nursing-care Quality Improvement System” have been proposed and operating contin...

In this paper, we propose a method of nursing-care text classification. We have proposed some nursing-care classification methods using fuzzy systems, standard three-layer neural networks, and support vector machines. Also we have proposed several types of feature vector definitions for expressing free style Japanese texts into numerical vectors. This paper proposes a novel feature vector definition and a support vector machine utilizing a decision tree (SVM-BDT) based classification system. From experimental results, the effectiveness of both feature definition and SVM-BDT-based classification system is shown.

In rhythmical gymnastics, movement should flow from the center of the body to peripheral parts of the body. VTR, DVD and photographs are used to teach the movements. The purpose of this study is to compare the impressions of anaglyph stereo images and 2D images using a questionnaire. The stretching and stability of a hard to understand movement was better perceived via anaglyph stereo image. For simple movements, anaglyph stereo images revealed dynamics more effectively than 2D images, but were less effective at revealing relaxing. This suggests that anaglyph stereo images are effective tools for analyzing movement in rhythmical gymnastics.

In the aging society such as Japan, it is very important to improve the quality of nursing-care for keeping our quality of life. Our final goal is to develop a computer aided evaluation system to improve the quality of nursing-care. For evaluating the quality of actual nursing, we have been collecting texts that are written by nurses using our Web based system. In our previous works, a SVM based classification system has been developed to classify such nursing-care texts, and a dependency relation based feature vector definition has also been proposed. The training data are pre-classified texts by a few nursing-care experts. Some texts in the training data are similar but classified into different classes. To classify the nursing-care texts with high accuracy, we need to tackle such ambiguous class labels in the training data. In this paper, we propose a k-nearest neighbor based classification system which can classify into classes with certainty grade.

Digital Video Recorder is widely used to visualize the dynamic image of moving body. In the field of motion science, DVR is often applied to study two and three-dimensional moving images of the human body under walking, running and jumping by means of several tracer markers on the body surface. In this study, the high-speed infrared radiometer IR simultaneously visualized the visual and thermal image of the moving human body using thermal marker and thermal footmarks. Characteristics of single- and multi-flash imaging methods were evaluated using the dynamic motion recorder.

Recently, “Web based Nursing-care Quality Improvement System” have been proposed and operating continuously for improving the nursing-care quality in Japan. For evaluating actual nursing-care process, freestyle Japanese texts which are called “nursing-care texts” are collected through the Internet. The nursing-care experts read the collected nursing-care texts carefully to evaluate actual nursing-care process. Then they make a recommendation which includes some improvements, and send it to each nurse. The number of nursing-care experts who can evaluate the nursing-care texts is a few. Hence, it is hard to perform the above mentioned evaluation process because of a large number of nurses. In order to assist nursing-care experts in evaluating the nursing-care texts, we have been developing a computer aided nursing-care text classification system. In this paper, first, we introduce our computer aided nursing-care text classification system. Then we propose a method to improve the classification performance of the nursing-care text classification system. In our proposed method, dependency relation between terms is extracted from the nursing-care text The extracted dependency is used as a feature value which represents characteristics of each nursing-care text. From some experimental results for the actual nursing-care text sets, we show that our proposed feature definition is effective for improving the classification performance.

In this paper, for improving performance of the nursing-care text classification, we introduce a mechanism of retrieving terms from Web. Every year, the nursing-care texts are collected by using Web application to improve nursing-care quality in Japan. The collected nursing-care texts are decomposed into morphemes (i.e., terms), and then terms are stored as a term list. Each text is represented as a feature vector by using the term list and classified using a SVM based classification system. The training data sets for constructing SVM based classification system are different from the evaluation data sets. That is, there are differences between the term lists of the nursing-care texts because the nursing-care texts are collected and evaluated every year. To cover this difference, we introduce a mechanism of retrieving terms from Web. A new term which appeared in the evaluation data sets is used as a query of a search engine. The terms in the term list are also used as queries. Terms are represented by the search results, and then are compared with each other. We use the most similar term in the term list as an alternative of the new term. From experimental results, we show effectiveness of our proposed method.

The nursing care quality improvement is very important for our life. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. The collected nursing-care data are stored into the database. To evaluate nursing-care data, we have already proposed a fuzzy classification system, a neural network based system, a support vector machine (SVM) based classification system. Then, in order to improve the classification performance, we have proposed a genetic algorithm (GA) based feature selection method for generating numerical data from collected nursing-care texts.In this paper, we propose a fuzzy rule extraction method from the nursing-care text data. First, features of nursing-care texts are selected by a genetic algorithm based feature selection method. Next, numerical training data are generated by using selected features. Then we train neural networks using generated training data. Finally, fuzzy if-then rules are extracted from the trained neural networks by the parallelized rule extraction method.From computer simulation results, we show the effectiveness of our proposed method.

The nursing care quality improvement is very important in the medical field. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. Some nursing-care experts evaluate the collected data to improve nursing care quality. For evaluating the nursing-care data, experts need to read all freestyle texts carefully. However, it is a hard task for an expert to evaluate the data because of huge number of nursing-care data in the database. In order to reduce workloads evaluating nursing-care data, we propose a support vector machine(SVM) based classification system.

Nursing-care data in this paper are Japanese texts written by nurses which consist of answers for questions about nursing-care. The nursing-care data are collected via WWW application from many hospitals in Japan. The collected data are stored into the database. The nursing-care experts evaluate the collected data to improve nursing-care quality. Currently, the collected data are evaluated by experts reading all texts carefully. It is difficult, however, for experts to evaluate the data because there are huge number of nursing-care data in the database. In this paper, to reduce workloads for the evaluation of nursing-care data, neural networks are used for classifying nursing-care data instead of fuzzy classification system. We use standard three-layer feedforward neural networks with back-propagation type learning. First, we extract attribute values (i.e., training data) from texts written by nurses. And then, we train a neural network using the training data. From computer simulations, we show the effectiveness of our proposed system using the leaving-one out method.

Processing the data from dental health examinations requires detailed procedures for many items. In order to use the computed data to the best advantage for regional health programs and for personal health guidance, the arrangement of our work environment to enable precise data processing is an urgent need. For this, we used to process examination data on the main computer system of the Computer Center, at the University of Tokyo. However, preparing a file for the data processing on a computer is always annoying and time consuming. Therefore, we designed here an input software to directly prepare a data file on a personal computer, and put it to trial in an actual dental health examination. We were able to file and process the examination data very quickly. The application of a computer to dental health examinations is thus very useful and helpful for further development of dental health science