A Sensor-Based Scheme for Activity Recognition in Smart Homes using Dempster-Shafer Theory of Evidence

Abstract

This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using beta probability distribution function. The frames of opinion owners are derived automatically for activities, to achieve more flexibility and extensibility. Our method is verified via two experiments. In the first experiment, it is compared to a naïve Bayes approach and three ontology based methods. In this experiment our method outperforms the naïve Bayes classifier, having 88.9% accuracy. However, it is comparable and similar to the ontology based schemes, but since no manual ontology definition is needed, our method is more flexible and extensible than the previous ones. In the second experiment, a larger dataset is used and our method is compared to three approaches which are based on naïve Bayes classifiers, hidden Markov models, and hidden semi Markov models. Three features are extracted from sensors' data and incorporated in the benchmark methods, making nine implementations. In this experiment our method shows an accuracy of 94.2% that in most of the cases outperforms the benchmark methods, or is comparable to them.