Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems

  • Julien Pansiot
  • Danail Stoyanov
  • Douglas McIlwraith
  • Benny P.L. Lo
  • G. Z. Yang
Part of the IFMBE Proceedings book series (IFMBE, volume 13)

Abstract

The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone.

Keywords

blob sensor wearable sensor sensor fusion activity recognition 

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Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  • Julien Pansiot
    • 1
    • 2
  • Danail Stoyanov
    • 1
    • 2
  • Douglas McIlwraith
    • 1
  • Benny P.L. Lo
    • 1
    • 2
  • G. Z. Yang
    • 1
    • 2
  1. 1.Royal Society/Wolfson MIC Lab, Department of ComputingImperial College LondonLondonUK
  2. 2.Institute of Biomedical EngineeringImperial College LondonUK

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