Reference : Adaptive Activity and Context Recognition using Multimodal Sensors in Smart Devices

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/22108

Title :	Adaptive Activity and Context Recognition using Multimodal Sensors in Smart Devices
Language :	English
Author, co-author :	Faye, Sébastien [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Frank, Raphaël [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Nov-2015
Journal title :	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Mobile Computing, Applications, and Services
Publisher :	Springer International Publishing
Peer reviewed :	Yes
Audience :	International
ISSN :	1867-8211
Event name :	7th International Conference on Mobile Computing, Applications and Services (MobiCASE '15)
Event date :	from 12-11-2015 to 13-11-2015
Event place (city) :	Berlin
Event country :	Germany
Keywords :	[en] Activity Recognition ; Wearable & Mobile Computing ; Sensing Systems
Abstract :	[en] The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today's smartphones have the potential to monitor and record a complete history of their owners' movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect different activities and associated physiological patterns. Next, the use of a scalable and energy-efficient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence.
Research centres :	SnT
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/22108
DOI :	10.1007/978-3-319-29003-4_3
Other URL :	http://link.springer.com/chapter/10.1007%2F978-3-319-29003-4_3

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