Using the eServices Platform for Detecting Behavior Patterns Deviation in the Elderly Assisted Living: A Case Study.
World’s aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderly’s behavio...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 11 |
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| Autores principales: | , , , , , |
| Formato: | case study tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/22/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273735&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273735 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/22/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273735 109273735 109273735 10.1155/2015/530828 109273735 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Using the eServices Platform for Detecting Behavior Patterns Deviation in the Elderly Assisted Living: A Case Study. aug: au: Marcelino, Isabel Lopes, David Reis, Michael Silva, Fernando Laza, Rosalía Pereira, António affil: Higher Technical School of Computer Engineering, University of Vigo, Polytechnic Building, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain sug: subj: Assisted Living In Old Age Behavior and Behavior Mechanisms Health Information Systems Risk Assessment Gerontologic Care Monitoring, Physiologic Aged Aged, 80 and Over Aged: 65+ years Aged, 80 & over ab: World’s aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderly’s behavior pattern deviations that may indicate possible risk situations. To obtain these patterns, many variables are aggregated to ensure the alert system reliability and minimize eventual false positive alert situations. These variables comprehend information provided by body area network (BAN), by environment sensors, and also by the elderly’s interaction in a service provider platform, called eServices—Elderly Support Service Platform. eServices is a scalable platform aggregating a service ecosystem developed specially for elderly people. This pattern recognition will further activate the adequate response. With the system evolution, it will learn to predict potential danger situations for a specified user, acting preventively and ensuring the elderly’s safety and well-being. As the eServices platform is still in development, synthetic data, based on real data sample and empiric knowledge, is being used to populate the initial dataset. The presented work is a proof of concept of knowledge extraction using the eServices platform information. Regardless of not using real data, this work proves to be an asset, achieving a good performance in preventing alert situations. pubtype: Academic Journal doctype: case study tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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