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Background: Several countries have recently attempted to implement telecare information technology to provide health care to older adults. This study applied self-determination theory (autonomy, relatedness, and competence) and the theory of planned behavior (subjective norm, perceived behavioral control, and attitudes toward using tools) to investigate a theoretical model for explaining the predictive factors influencing the intention of elderly patients to continue using telecare services.

Methods: Elderly patients in Taiwan (N = 160) who used telecare systems and fall-detection systems completed a questionnaire. Hierarchical multiple regression analysis was applied to test hypotheses.

Results: The results revealed that the main effects related to identification supported the notion that autonomy, relatedness, subjective norm, and attitudes toward using tools positively affect elderly patients' intention to continue using telecare services. But, perceived competence and perceived behavioral control cannot be used as a predictor of intention to adopt telecare services.

Conclusion: For an aging society, to provide appropriate ways to enhance elderly patients' willingness to use telecare services is important. Our findings indicate that elderly patients' perceived relatedness and subjective norm are both crucial predictors in intention to adopt telecare services. And it means that social influence may play a critical role in elderly patients' intention to adopt telecare services; therefore, researchers can investigate social influence mechanisms in depth and examine them more closely in future research.

Background: Health problems about children have been attracting much attention of parents and even the whole society all the time, among which, child-language development is a hot research topic. The experts and scholars have studied and found that the guardians taking appropriate intervention in children at the early stage can promote children's language and cognitive ability development effectively, and carry out analysis of quantity. The intervention of Artificial Intelligence Technology has effect on the autistic spectrum disorders of children obviously.

Objective and methods: This paper presents a speech signal analysis system for children, with preprocessing of the speaker speech signal, subsequent calculation of the number in the speech of guardians and children, and some other characteristic parameters or indicators (e.g cognizable syllable number, the continuity of the language).

Results: With these quantitative analysis tool and parameters, we can evaluate and analyze the quality of children's language and cognitive ability objectively and quantitatively to provide the basis for decision-making criteria for parents. Thereby, they can adopt appropriate measures for children to promote the development of children's language and cognitive status.

Conclusion: In this paper, according to the existing study of children's language development, we put forward several indicators in the process of automatic measurement for language development which influence the formation of children's language. From the experimental results we can see that after the pretreatment (including signal enhancement, speech activity detection), both divergence algorithm calculation results and the later words count are quite satisfactory compared with the actual situation.