Effects of smart home dataset characteristics on classifiers performance for human activity recognition

Abstract

Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. A list of machine learning algorithms is available for activity classification. Datasets collected in smart homes poses unique challenges to these methods for classification because of their high dimensionality, multi-class activities and various deployed sensors. In fact the nature of dataset plays considerable role in recognizing the activities accurately for a particular classifier. In this paper, we evaluated the effects of smart home datasets characteristics on state-of-the-art activity recognition techniques. We applied probabilistic and statistical methods such as the Artificial Neural network, Hidden Markov Model, Conditional Random Field, and Support Vector Machines. The four real world datasets are selected from three most recent and publically available smart home projects. Our experimental results show that how the performance of activity classifiers are influenced by the dataset characteristics. The outcome of our study will be helpful for upcoming researchers to develop a better understanding about the smart home datasets characteristics in combination with classifier's performance. © 2012 Springer Science+Business Media.