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

This paper proposes a scheme for daily activity recognition in sensor-based smart homes using Dempster- Shafer theory of evidence. For this purpose, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using the 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 with 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. 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 with three approaches that are based on naïve Bayes classifiers, hidden Markov models, and hidden semi-Markov models. Three features are extracted from the 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 cases, outperforms the benchmark methods or is comparable to them.