Feature set selection and optimal classifier for human activity recognition

Human activity recognition is an essential ability for service robots and other robotic systems which are in interaction with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if realtime constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a thorough analysis of features and classifiers aimed at human activity recognition is presented. Based on a set of 10 activities, the use of different feature selection algorithms is evaluated, as well as the results different classifiers (SVMs, Neural Networks, Bayesian Classifiers) provide in this context. Also the interdependency between feature selection method and chosen classifier is investigated. Furthermore, the optimal number of features to be used for an activity is examined.