Testing and analysis of the proposed data driven method on the opportunity human activity dataset

Abstract

This paper proposes a data-driven method for constructing materials to be used in a probabilistic knowledge base for human activity recognition. The utilized dataset, challenge subset of Opportunity, is a publicly available dataset. It consists of a set of daily activities, which has been manually labeled as modes of locomotion and gestures. We applied several methods to extract proper features from sensors on bodies of subjects, then, chosen features are fed into two different classifiers. Finally, predicted labels for modes of locomotion and hand gestures are calculated. To evaluate the method, the recognition rates are bench marked against the results of the competitors who have participated in Opportunity challenge as well as the baseline results provided by the Opportunity group. For modes of locomotion, our results surpass all of the available results and in some cases the recognition rate of our model is very close to the highest recognition rate. For gestures, regular or noisy data,in some cases our method is still higher than baseline or challenge participants but unlike locomotion, it is not capable to beat them all.