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Towards Smart Homes Using Low Level Sensory Data
Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251999/ https://www.ncbi.nlm.nih.gov/pubmed/22247682 http://dx.doi.org/10.3390/s111211581 |
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author | Khattak, Asad Masood Truc, Phan Tran Ho Hung, Le Xuan Vinh, La The Dang, Viet-Hung Guan, Donghai Pervez, Zeeshan Han, Manhyung Lee, Sungyoung Lee, Young-Koo |
author_facet | Khattak, Asad Masood Truc, Phan Tran Ho Hung, Le Xuan Vinh, La The Dang, Viet-Hung Guan, Donghai Pervez, Zeeshan Han, Manhyung Lee, Sungyoung Lee, Young-Koo |
author_sort | Khattak, Asad Masood |
collection | PubMed |
description | Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules. |
format | Online Article Text |
id | pubmed-3251999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32519992012-01-13 Towards Smart Homes Using Low Level Sensory Data Khattak, Asad Masood Truc, Phan Tran Ho Hung, Le Xuan Vinh, La The Dang, Viet-Hung Guan, Donghai Pervez, Zeeshan Han, Manhyung Lee, Sungyoung Lee, Young-Koo Sensors (Basel) Article Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules. Molecular Diversity Preservation International (MDPI) 2011-12-12 /pmc/articles/PMC3251999/ /pubmed/22247682 http://dx.doi.org/10.3390/s111211581 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Khattak, Asad Masood Truc, Phan Tran Ho Hung, Le Xuan Vinh, La The Dang, Viet-Hung Guan, Donghai Pervez, Zeeshan Han, Manhyung Lee, Sungyoung Lee, Young-Koo Towards Smart Homes Using Low Level Sensory Data |
title | Towards Smart Homes Using Low Level Sensory Data |
title_full | Towards Smart Homes Using Low Level Sensory Data |
title_fullStr | Towards Smart Homes Using Low Level Sensory Data |
title_full_unstemmed | Towards Smart Homes Using Low Level Sensory Data |
title_short | Towards Smart Homes Using Low Level Sensory Data |
title_sort | towards smart homes using low level sensory data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251999/ https://www.ncbi.nlm.nih.gov/pubmed/22247682 http://dx.doi.org/10.3390/s111211581 |
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