Intent Inference of Human Hand Motion for Haptic Feedback Systems

Abstract

The haptic feedback system (HFS) in the virtual cockpit system (VCS) can definitely enhance the sense of immersion. Most HFSs in prior works sacrificed the native advantages of VCSs to achieve haptic interaction. This paper addresses the problem by proposing a novel framework for the HFS, which can predict the most likely interacting target of the human hand in advance. We introduce a HFS with a non-contact visual tracking sensor and a probability inference method based on Bayesian statistics, the features extracted by this HFS could be low-cost, high generality and flexibility. Simulations show that human intent inference can be computed in real-time and the results can meet the requirements of the HFM, which provides an important basis for haptic interactions in VCSs.