StreamAR: incremental and active learning with evolving sensory data for activity recognition

Z. Abdallah, M. Gaber, B. Srinivasan, S. Krishnaswamy

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Abstract

Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition Systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.
Original languageEnglish
Publication statusPublished - 7 Nov 2012
EventProceedings of the 24th IEEE International Conference on Tools with Artificial Intelligence - Athens, Greece
Duration: 7 Nov 20129 Nov 2012

Conference

ConferenceProceedings of the 24th IEEE International Conference on Tools with Artificial Intelligence
Country/TerritoryGreece
CityAthens
Period7/11/129/11/12

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