Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64646
Title: Methodologies for personalized well-being assessment system using multi-modal sensors
Authors: Xu, Minghan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2015
Abstract: Personal well-being is the comprehensive integration of mental state and physical health. Its conventional methods of measurement rely on survey and questionnaire that are ineffective and challenging for long-term monitoring. The usual approach is to examine the components of well-being separately and integrated intelligence is sometimes missing. As an interdisciplinary subject, its advancement can leverage on the development of sensing technology. By replacing the paper-based evaluation questionnaire, a real-time tracking system that integrates personal physical activity and environmental quality was proposed in this project. Hence the evaluation of personal well-being stretches from single dimension, which is the fitness aspect, to multidimensions that combine both fitness and environmental factors. The objective of this project is to research, suggest and prototype a system that not only provides tracking service to some of the key areas of personal well-being, but also helps users understand themselves and their interaction with the surrounding environment. Extensive and crossdisciplinary literature studies have been conducted to understand the movement tracking mechanism, established well-being benchmark, recommended physical activity level and suggested environmental indices. A solution was proposed from the research outcomes and a prototype was assembled, tested and evaluated. At the end of this project, future research areas have been identified to provide directions for the next batch of researchers or students who continue working on this project.
URI: http://hdl.handle.net/10356/64646
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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