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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/98241


    Title: Daily Health Assessment System Using Prediction Model for Self-rated Health by Vital Sign Pattern
    Authors: Huang, Kuan-Ling;Chen, Ya-Hung;Liao, Chun-Feng;Fu, Li-Chen
    廖峻鋒
    Contributors: 資科系
    Date: 2014-10
    Issue Date: 2016-06-22 17:15:34 (UTC+8)
    Abstract: With the growing population of aging people around the world, there is an increasing needs for elders to be aware of their health status not only in hospital but also in home environment. Due to recent advances of health monitoring technologies, elder people are able to easily assess their physiological well-being at home. However, most of current physiological monitoring systems focus on the patient with critical situation such as the intensive care unit and there are relatively fewer systems aim to assess these trends in home scenario. Additionally, most of the current home vital sign monitoring systems use pre-determined threshold to identify the dangerous situation over single measurement or just focus on the system architecture as well as communication technique. It is essential to develop a healthcare system that is capable to make an alarm far before elderly people is in acute situation. In this paper, a data model is built for the vital sign trends over certain number of days. In order to determine the dangerous situation, this model is associated with a common health assess tool - self-rated health, which has been confirmed a predictor for mortality over elder population. To demonstrate the feasibility of our system, four subjects aging from 58 to 95 had been participated in experiment and collected vital sign once a day. The result shows that the proposed system is able to identify the poor health condition based on the collected data with high precision.
    Relation: Proc. IEEE Healthcare Innovation Point-Of-Care Technologies Conference, Seattle, Washington, USA, 2014, 95-98
    Data Type: conference
    DOI link: http://dx.doi.org/10.1109/HIC.2014.7038883
    DOI: 10.1109/HIC.2014.7038883
    Appears in Collections:[Department of Computer Science ] Proceedings

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