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Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems

Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems

Izabella V. Lokshina, Michael R. Bartolacci
Copyright: © 2019 |Pages: 23
ISBN13: 9781522581888|ISBN10: 152258188X|ISBN13 Softcover: 9781522586012|EISBN13: 9781522581895
DOI: 10.4018/978-1-5225-8188-8.ch015
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MLA

Lokshina, Izabella V., and Michael R. Bartolacci. "Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems." Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking, edited by Natarajan Meghanathan, IGI Global, 2019, pp. 290-312. https://doi.org/10.4018/978-1-5225-8188-8.ch015

APA

Lokshina, I. V. & Bartolacci, M. R. (2019). Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems. In N. Meghanathan (Ed.), Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking (pp. 290-312). IGI Global. https://doi.org/10.4018/978-1-5225-8188-8.ch015

Chicago

Lokshina, Izabella V., and Michael R. Bartolacci. "Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems." In Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking, edited by Natarajan Meghanathan, 290-312. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8188-8.ch015

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Abstract

This chapter explains eHealth; discusses experiences, health management strategies, and healthcare models to address overweight and obesity in young population; and focuses on mathematical background of individual health status monitoring system to empower young people to manage their health. The proposed system uses symptoms observed with mobile sensing devices to define individual physical and psychological status. It has flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations towards healthy lifestyles is reached and correct interpretation is guaranteed. Models and algorithms are developed based on the composition inference rule in fuzzy logic that makes health status identification process faster and obtained results more precise and efficient comparing to traditional identification algorithms.

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