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.
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© 2007 International Federation for Medical and Biological Engineering
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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. https://doi.org/10.1007/978-3-540-70994-7_36
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DOI: https://doi.org/10.1007/978-3-540-70994-7_36
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