Adaptive mobile activity recognition system with evolving data streams

Zahraa S. Abdallah, Mohamed Medhat Gaber, B Srinivasan, Shonali Krishnaswamy

Research output: Contribution to journalArticle (Academic Journal)

58 Citations (Scopus)

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 datastream. 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.
Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusPublished - 2015

Fingerprint Dive into the research topics of 'Adaptive mobile activity recognition system with evolving data streams'. Together they form a unique fingerprint.

  • Cite this