Hand Motion Prediction for Distributed Virtual Environments

We use our hands to manipulate objects in our daily life. The hand is capable of accomplishing diverse tasks such as pointing, gripping, twisting, and tearing. However, there is not much work that considers using the hand as input in distributed virtual environments (DVEs), in particular, over the Internet. The main reasons are that the Internet suffers from high network latency, which affects interaction, and the hand has many degrees of freedom, which adds additional challenges to synchronizing the collaboration. In this paper, we propose a prediction method specifically designed for human hand motion to address the network latency problem in DVEs. Through a thorough analysis of finger motion, we have identified various finger motion constraints, and we propose a constraint-based motion prediction method for hand motion. To reduce the average prediction error under high network latency, for example, over the Internet, we further propose a revised dead-reckoning scheme here. Our performance results show that the proposed prediction method produces a lower prediction error than some popular methods, and the revised dead-reckoning scheme produces a lower average prediction error than the traditional dead-reckoning scheme, particularly at high network latency.