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ISRN Artificial Intelligence
Volume 2013 (2013), Article ID 380239, 11 pages
http://dx.doi.org/10.1155/2013/380239
Research Article

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

Center for Applied Autonomous Sensor Systems, Örebro University, 701 82 Örebro, Sweden

Received 24 March 2013; Accepted 18 April 2013

Academic Editors: T.-C. Chen, G. L. Foresti, Z. Liu, and R. Rada

Copyright © 2013 Mobyen Uddin Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.