Data model for health telemonitoring and persuasive system design. (2017)

• Record Type: Journal Article
• Authors: Maret, Pierre; Pawar, Pravin Amrut; Warisawa, Shin'ichi; Muhlenbach, Fabrice; Lopez, Guillaume; Yamada, Ichiro
• Is Part Of: International journal of intelligent information and database systems. Volume 10:Number 1/2(2017)
• Page Start: 4
• Page End: 20
• Publication Date: 2017
• Subjects: sensor data -- health telemonitoring -- data model -- data mining -- data granularity -- privacy management -- persuasive systems; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

The health telemonitoring systems are expected to accomplish two basic tasks: continuously collect data from data sources wherever they may be; and allow remote communication between the stakeholders to access data. This data is further used by decision making systems to infer a patient's condition and possibly to motivate a patient to follow a healthy lifestyle by using persuasive system approach. The implementation and maintenance of such systems require specific attention of software engineers for data management because of the complexity involved in the management of various data sources and privacy-related issues of personal data. In this paper, we propose a data model that is generic enough to describe and to support many kinds of telemonitoring applications. These applications include data mining applications which analyse and combine sensor data for a decision making, and persuasive systems which motivate a patient to follow a healthy lifestyle. We show that our data model is useful for a smooth management of data mining outputs and that it avoids the integration efforts in dealing with heterogeneous storage mechanisms. We also show that our data model eases the management of the granularity of data and that it facilitates software designers' tasks for the implementation of privacy protection mechanisms.