Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Kyoto University, Nagoya University, Kyushu University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2950512464