Australasian Joint Conference on Artificial Intelligence

AI 2015: Advances in Artificial Intelligence pp 505-516 | Cite as

Event Classification Using Adaptive Cluster-Based Ensemble Learning of Streaming Sensor Data

  • Ahmad Shahi
  • Brendon J. Woodford
  • Jeremiah D. Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9457)

Abstract

Sensor data stream mining methods have recently brought significant attention to smart homes research. Through the use of sliding windows on the streaming sensor data, activities can be recognized through the sensor events. However, it remains a challenge to attain real-time activity recognition from the online streaming sensor data. This paper proposes a new event classification method called Adaptive Cluster-Based Ensemble Learning of Streaming sensor data (ACBEstreaming). It contains desirable features such as adaptively windowing sensor events, detecting relevant sensor events using a time decay function, preserving past sensor information in its current window, and forming online clusters of streaming sensor data. The proposed approach improves the representation of streaming sensor-events, learns and recognizes activities in an on-line fashion. Experiments conducted using a real-world smart home dataset for activity recognition have achieved better results than the current approaches.

Keywords

On-line learning Streaming Activity recognition Smart home 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ahmad Shahi
    • 1
  • Brendon J. Woodford
    • 1
  • Jeremiah D. Deng
    • 1
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand

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