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
Conference paper

DOI: 10.1007/978-3-540-70994-7_36

Part of the IFMBE Proceedings book series (IFMBE, volume 13)
Cite this paper as:
Pansiot J., Stoyanov D., McIlwraith D., Lo B.P., Yang G.Z. (2007) Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems. In: Leonhardt S., Falck T., Mähönen P. (eds) 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007). IFMBE Proceedings, vol 13. Springer, Berlin, Heidelberg

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
    • 2
    • 3
  • Danail Stoyanov
    • 2
    • 3
  • Douglas McIlwraith
    • 2
  • Benny P.L. Lo
    • 2
    • 3
  • G. Z. Yang
    • 2
    • 3
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Royal Society/Wolfson MIC Lab, Department of ComputingImperial College LondonLondonUK
  3. 3.Institute of Biomedical EngineeringImperial College LondonUK

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