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Smartphone derived movement profiles to detect changes in health status in COPD patients-A preliminary investigation

Authors: 

Daniel Kelly, Seamas Donnelly, Brian Caulfield

Publication Type: 
Refereed Original Article
Abstract: 
Over 3.2 million people in the UK alone have the lung disease Chronic Obstructive Pulmonary Disease. Identifying when COPD patients are at risk of an exacerbation is a major problem and there is a need for smart solutions that provide us with a means of tracking patient health status. Smart-phone sensor technology provides us with an opportunity to automatically monitor patients. With sensors providing the ability to measure aspects of a patient's daily life, such a motion, methods to interpret these signals and infer health related information are needed. In this work we aim to investigate the feasibility of utilizing motion sensors, built within smartphones, to measure patient movement and to infer the health related information about the patient. We perform experiments, based on 7 COPD patients using data collected over a 12 week period for each patient, and identify a measure to distinguish between periods when a patient feels well Vs periods when a patient feels unwell.
Digital Object Identifer (DOI): 
10.1109/EMBC.2015.7318399
Publication Status: 
Published
Date Accepted for Publication: 
Friday, 12 December, 2014
Publication Date: 
01/01/2015
Journal: 
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No