Reference : Using Hidden Markov Models and Rule-based Sensor Mediation on Wearable eHealth Devices

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/33264

Title :	Using Hidden Markov Models and Rule-based Sensor Mediation on Wearable eHealth Devices
Language :	English
Author, co-author :	Neyens, Gilles [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Zampunieris, Denis [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2017
Main document title :	Procedings of the 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Barcelona, Spain 12-16 November 2017
Publisher :	IARIA
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-61208-598-2
Event name :	UBICOMM 2017 - 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
Event date :	November 2017
Event organizer :	IARIA
Event place (city) :	Barcelona
Event country :	Spain
Keywords :	[en] Wearable devices ; Conflict handling ; Hidden Markov Model ; Autonomic Computing ; Rule-based Systems ; Sensor Mediation ; Proactive Computing
Abstract :	[en] Improvements in sensor miniaturization allow wearable devices to provide more functionality while also being more comfortable for users to wear. The Samsung Simband©, for example, has 6 different sensors Electrocardiogram (ECG), Photoplethysmogram (PPG), Galvanic Skin Response (GSR), Bio-Impedance (Bio-Z), Accelerometer and a thermometer as well as a modular sensor hub to easily add additional ones. This increased number of sensors for wearable devices opens new possibilities for a more precise monitoring of patients by integrating the data from the different sensors. This integration can be influenced by failing or malfunctioning sensors and noise. In this paper, we propose an approach that uses Hidden Markov Models (HMM) in combination with a rule-based engine to mediate among the different sensors’ data in order to allow the eHealth system to compute a diagnosis on the basis of the selected reliable sensors. We also show some preliminary results about the accuracy of the first stage of the proposed model.
Permalink :	http://hdl.handle.net/10993/33264
Commentary :	Procedings of the 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Barcelona, Spain 12-16 November 2017

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