An online feature selection architecture for Human Activity Recognition

Human Activity Recognition (HAR) must currently face up to the challenge of rethinking analytics from the perspective of real-time operation, wherein biophysical sensing streams are efficiently intertwined at close vicinity to the point of sensing. As such, feature selection techniques, traditionally employed for off-line data processing, should be evaluated with respect to their ability to filter out redundant information in real-time. In this work, we propose an online architecture for implementing feature selection on mobile devices, and we evaluate popular feature selection methods against constantly alternating activity labels. We perform a qualitative analysis to determine the dominant sensing modality that dictates the activities of a certain time duration. The results indicate that online feature selection performance changes among consecutive data partitions, leading to the conclusion that the type of available activity influences significantly the feature selection procedure.