A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

Authors:
Dung T. Tran;Dinh Q. Phung
Affiliations:
Curtin University of Technology, Western Australia;Curtin University of Technology, Western Australia
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 2

Assessment of the Impact of Sensor Failure in the Recognition of Activities of Daily Living

ICOST '08 Proceedings of the 6th international conference on Smart Homes and Health Telematics
Evidential fusion of sensor data for activity recognition in smart homes

Pervasive and Mobile Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.