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Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis

Authors:


Publication Type:

Journal article

Venue:

ISRN Artificial Intelligence

DOI:

http://dx.doi.org/10.1155/2013/380239


Abstract

This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their similarity. Record collection has been performed using a personalized health profiling approach in which participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction methods in time, frequency, and time-frequency domains, as well as data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates an alarm according to the case outcomes. The system has been compared with an expert's classification, and a 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements, the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in the analysis of continuous health monitoring and can be used as a suitable method in home/remote monitoring systems.

Bibtex

@article{Ahmed3778,
author = {Mobyen Uddin Ahmed and Hadi Banaee and Amy Loutfi },
title = {Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis},
volume = {2013},
number = {2013},
pages = {1--11},
month = {May},
year = {2013},
journal = {ISRN Artificial Intelligence },
url = {http://www.es.mdu.se/publications/3778-}
}