Applying hierarchical information with learning approach for activity recognition
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Weight factor algorithms for activity recognition in lattice-based sensor fusion
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
A comprehensive machine learning approach to prognose pulmonary disease from home
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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This paper explores a sensor fusion method applied within smart homes used for the purposes of monitoring human activities in addition to managing uncertainty in sensor-based readings. A three-layer lattice structure has been proposed, which can be used to combine the mass functions derived from sensors along with sensor context. The proposed model can be used to infer activities. Following evaluation of the proposed methodology it has been demonstrated that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and subsequently infer the activity using the proposed lattice structure. The results from this study show that this method can detect a toileting activity within a smart home environment with an accuracy of 88.2%.