Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis. (22nd May 2013)

• Record Type: Journal Article
• Authors: Ahmed, Mobyen Uddin; Banaee, Hadi; Loutfi, Amy
• Other Names: Chen T.-C. Academic Editor.; Foresti G. L. Academic Editor.; Liu Z. Academic Editor.; Rada R. Academic Editor.
• Is Part Of: ISRN artificial intelligence. Volume 2013(2013)
• Publication Date: 2013-05-22
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; 006.3
• DOI: 10.1155/2013/380239
• Languages: English
• ISSNs: 2090-7435

Abstract

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.