EEMCS

Home > Publications
Home University of Twente
Education
Research
Prospective Students
Jobs
Publications
Intranet (internal)
 
 Nederlands
 Contact
 Search
 Organisation

EEMCS EPrints Service


19553 Predicting Feedback Compliance in a Teletreatment Application
Home Policy Brochure Browse Search User Area Contact Help

op den Akker, H. and Jones, V.M. and Hermens, H.J. (2010) Predicting Feedback Compliance in a Teletreatment Application. In: 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010, 7-10 Nov 2010, Rome, Italy. pp. 1-5. IEEE. ISBN 978-1-4244-8131-6

Full text available as:

PDF

101 Kb
Open Access



Official URL: http://dx.doi.org/10.1109/ISABEL.2010.5702804

Exported to Metis

Abstract

Health care provision is facing resourcing challenges which will further increase in the 21st century. Health care mediated by technology is widely seen as one important element in the struggle to maintain existing standards of care. Personal health monitoring and treatment systems with a high degree of autonomic operation will be required to support self-care. Such systems must provide many services and in most cases must incorporate feedback to patients to advise them how to manage the daily details of their treatment and lifestyle changes. As in many other areas of healthcare, patient compliance is however an issue. In this experiment we apply machine learning techniques to three corpora containing data from trials of body worn systems for activity monitoring and feedback. The overall objective is to investigate how to improve feedback compliance in patients using personal monitoring and treatment systems, by taking into account various contextual features associated with the feedback instances. In this article we describe our first machine learning experiments. The goal of the experiments is twofold: to determine a suitable classification algorithm and to find an optimal set of contextual features to improve the performance of the classifier. The optimal feature set was constructed using genetic algorithms. We report initial results which demonstrate the viability of this approach.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-BSS: Biomedical Signals and Systems
Research Program:CTIT-ASSIST: Applied Science of Services for Information Society Technologies
Research Project:IS-ACTIVE: Inertial Sensing Systems for Advanced Chronic Condition Monitoring and Risk Prevention
Uncontrolled Keywords:Mobile healthcare, activity monitoring, feedback compliance, machine learning, genetic algorithms
ID Code:19553
Status:Published
Deposited On:03 February 2011
Refereed:Yes
International:Yes
More Information:statisticsmetis

Export this item as:

To correct this item please ask your editor

Repository Staff Only: edit this item