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, e.g., 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 while the revised dead reckoning scheme produces a lower average prediction error than the traditional dead reckoning scheme, in particular at high network latency.

A trajectory-preserving synchronization method for collaborative visualization.

In the past decade, a lot of research work has been conducted to support collaborative visualization among remote users over the networks, allowing them to visualize and manipulate shared data for problem solving. There are many applications of collaborative visualization, such as oceanography, meteorology and medical science. To facilitate user interaction, a critical system requirement for collaborative visualization is to ensure that remote users will perceive a synchronized view of the shared data. Failing this requirement, the user's ability in performing the desirable collaborative tasks will be affected. In this paper, we propose a synchronization method to support collaborative visualization. It considers how interaction with dynamic objects is perceived by application participants under the existence of network latency, and remedies the motion trajectory of the dynamic objects. It also handles the false positive and false negative collision detection problems. The new method is particularly well designed for handling content changes due to unpredictable user interventions or object collisions. We demonstrate the effectiveness of our method through a number of experiments.