Pansiot, J and Stoyanov, D and McIlwraith, D and Lo, BPL and Yang, GZ (2007) Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems. In: Leonhardt, S and Falck, T and Mahonen, P, (eds.) 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007). (pp. 208 - 212). SPRINGER
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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.
|Title:||Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems|
|Event:||4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007)|
|Location:||RWTH Aachen Univ, Aachen, GERMANY|
|Dates:||2007-03-26 - 2007-03-28|
|Keywords:||blob sensor, wearable sensor, sensor fusion, activity recognition|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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