Filippoupolitis, Avgoustinos, Takand, Babak and Loukas, George ORCID: 0000-0003-3559-5182 (2017) Activity recognition in a home setting using off the shelf smart watch technology. In: 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS). IEEE, pp. 39-44. ISBN 978-1-5090-5567-8 ISSN 978-1-5090-5566-1 (Online) (doi:https://doi.org/10.1109/IUCC-CSS.2016.014)

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Abstract

Being able to detect in real-time the activity per- formed by a user in a home setting provides highly valuable context. It can allow more effective use of novel technologies in a large variety of applications, from comfort and safety to energy efficiency, remote health monitoring and assisted living. In a home setting, activity recognition has been traditionally studied based on either a large sensor network infrastructure already set up in a home, or a network of wearable sensors attached to various parts of the user’s body. We argue that both approaches suffer considerably in terms of practicality and propose instead the use of commercial off-the-shelf smart watches, already owned by the users. We test the feasibility of this approach with two different smart watches of very different capabilities, on a variety of activities performed daily in a domestic environment, from brushing teeth to preparing food. Our experimental results are encouraging, as using stan- dard Support Vector Machine based classification, the accuracy rates range between 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation.

Item Type:	Conference Proceedings
Title of Proceedings:	15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS)
Additional Information:	Smart Ubiquitous Technologies for Human Behaviour Coaching Conference, 14-16 December 2016, Granada, Spain. In conjunction with the 15th International Conference on Ubiquitous Computing and Communications (IUCC-2016).
Uncontrolled Keywords:	Wearables; Activity recognition
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Related URLs:	Organisation Organisation
Last Modified:	04 Mar 2022 13:07
URI:	http://gala.gre.ac.uk/id/eprint/16028

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