Adaptive mobile activity recognition system with evolving data streams

Abdallah, Zahraa S. and Gaber, Mohamed Medhat and Srinivasan, Bala and Krishnaswamy, Shonali (2014) Adaptive mobile activity recognition system with evolving data streams. Neurocomputing, 150 (A). pp. 304-317. ISSN 0925-2312

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Abstract

Mobile activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich mobile phones. Supervised learning with static models has been applied pervasively for mobile activity recognition. In this paper, we propose a novel phone-based dynamic recognition framework with evolving data streams for activity recognition. The novel framework incorporates incremental and active learning for real-time recognition and adaptation in streaming settings. While stream evolves, we refine, enhance and personalise the learning model in order to accommodate the natural drift in a given data stream. Extensive experimental results using real activity recognition data have evidenced that the novel dynamic approach shows improved performance of recognising activities especially across different users.

Item Type:	Article
Identification Number:	10.1016/j.neucom.2014.09.074
Dates:	Date Event 13 September 2014 Accepted 18 October 2014 Published Online
Uncontrolled Keywords:	Ubiquitous computing Mobile application Activity recognition Stream mining Incremental learning Activelearning
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Ian Mcdonald
Date Deposited:	26 Jan 2017 11:54
Last Modified:	22 Mar 2023 12:02
URI:	https://www.open-access.bcu.ac.uk/id/eprint/3830

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