DocumentCode :
3141299
Title :
A Cloud-Based Accessible Architecture for Large-Scale ADL Analysis Services
Author :
Huang, Yu-Chiao ; Ho, Yu-Chieh ; Lu, Ching-Hu ; Fu, Li-Chen
Author_Institution :
Dept. of Comput. Sci. & Inf., Nat. Taiwan Univ., Taipei, Taiwan
fYear :
2011
fDate :
4-9 July 2011
Firstpage :
646
Lastpage :
653
Abstract :
Recognizing Activities of Daily Living (ADL) plays an important role in healthcare. However, it is often impractical and sometimes impossible for a person to collect those useful data manually, not to mention constant long-term data maintenance and analysis. To address the above-mentioned challenges, we propose an architecture, in which many health-care applications and services can easily build upon, for collective long-term ADL pattern analysis that leverages several prominent advantages inherent in cloud computing. The core of the proposed infrastructure includes a module to perform MapReduce-assisted Bayesian activity recognition based on all collected ADL data. Better yet, the resultant data analysis can be delivered as a service from a service station which serves as a readily accessible interface to 3rd party service providers and end-users. For the evaluation of the proposed architecture, a simulation of persuasive health engagement is presented and discussed as one potential application.
Keywords :
cloud computing; data analysis; health care; ADL analysis services; Bayesian activity recognition; activities of daily living; cloud computing; data analysis; data maintenance; healthcare; pattern analysis; Cloud computing; Computer architecture; Data analysis; Data mining; Engines; Medical services; Sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing (CLOUD), 2011 IEEE International Conference on
Conference_Location :
Washington, DC
ISSN :
2159-6182
Print_ISBN :
978-1-4577-0836-7
Electronic_ISBN :
2159-6182
Type :
conf
DOI :
10.1109/CLOUD.2011.97
Filename :
6008766
Link To Document :
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