Depression Index Service Using Knowledge Based Crowdsourcing in Smart Health

The development of the IT convergence technology has been serving as the basis for the application of the IoT devices to the products being used in our everyday life, such as clock, health band, scale, TV, light and doorlock. The government and companies have been developing the IoT-based smart home, smart city and smart health to enhance the national health as well as the life convenience. In particular, the government has been providing its diverse supports to the smart health in order to utilize electronic medical records, personal health record and self-diagnosis. The Ministry of Health and Welfare has been expanding the self-diagnosis service through conducting an epidemiologic survey on mental illness and mental health, the two issues that are becoming a major problem in our society. The results of the survey-based self-diagnosis may vary depending on the environment surrounding the users as well as the mental state of the users. In this paper, we propose the depression index service using the knowledge-based crowdsourcing within the smart health platform. The proposed method provides the index suitable for the context of each user through using the knowledge-based crowdsourcing within the smart health platform to compare the actual users with the similar users and predict the depression level. The prediction of the depression index uses the collaborative filtering model based on the comparison of users with other users, and uses the crowdsourcing to supplement the sparsity problem. CES-D is used as the criterion for determining the depression level, and the CES-D data is collected through the crowdsourcing. In the preexisting crowdsourcing process, the highly expert/reliable knowledge base to which the context information of the users is applied is constructed. The context information of the users is additionally collected through the self-diagnosis process, and the data are determined through the preconditioning process. The actual users data and determined data are applied to the Pearson’s correlation coefficient to analyze the similarity level of between the actual/preexisting users. Then the analyzed similarity is used to service the result of the self-diagnosis to the actual users. This is a flexible service to which the context information of the users is applied, and it contributes to the users’ decision-making.