Adaptive mobile activity recognition system with evolving data streams

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 dynam...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 150; S. 304 - 317
Hauptverfasser: Abdallah, Zahraa Said, Gaber, Mohamed Medhat, Srinivasan, Bala, Krishnaswamy, Shonali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 20.02.2015
Schlagworte:
Active learning
Activity recognition
Data transmission
Dynamical systems
Dynamics
Evolution
Incremental learning
Learning
Mobile application
Real time
Recognition
Stream mining
Streams
Ubiquitous computing
Activity recognition
Ubiquitous computing
Incremental learning
Mobile application
Stream mining
Active learning
ISSN:0925-2312, 1872-8286
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.09.074