  Hand orientation is an essential feature required to understand hand
behaviors and subsequently support human activities. In this paper, we present
a new method for estimating hand orientation in probability density form. It
can solve the cyclicity problem in direct angular representation and enables
the integration of multiple predictions based on different features. We
validated the performance of the proposed method and an integration example
using our dataset, which captured cooperative group work.
