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Interactions between a human operator and objects in his/her world afford a wide range of possible viewing and control options, which can vary with respect to time, space, proximity, and frame of reference. Whereas it is conventionally recognised that essentially any kind of interaction metaphor can in principle be simulated in a virtual environment, modern image processing technology now permits greatly increased flexibility also for real world interactions with indirect viewing, Ai as the common video camera has gone beyond being a simple eye on the (remote) real world to being an instrument that is able to integrate and interpolate real world images spatially and temporally, in real time.In the talk, I shall propose a framework for classifying viewing and manipulation interactions for any task where visual feed back is provided.The framework involves identifying key components of the environment that are central to the interaction, in terms of the multidimensional couplings among the components, where the configuration and characteristics of the couplings determine the nature of the visual and manual control interactions experienced by the operator. The framework is domain independent, and is intended to be used both to classify current display-control interactions and to identify future areas of research.

Coordination definitions and metrics are reviewed from the motor control, biomedical, and human factors literature. This paper presents an alternative measurement called the M-metric, the product of the simultaneity and efficiency of a trajectory, as a means of quantifying allocation of control within a docking task. A 6 degree-of-freedom (DOF) longitudinal virtual docking task experiment was conducted to address how control is allocated across six DOFs, how allocation of control changes with extended practice, and if differences in the allocation of control are input device dependent. The results show that operators, rather than controlling all 6 DOFs equally, allocate their control to the rotational and translational DOFs separately, and switch control between the two groups. With practice, allocation of control within the translational and rotational subsets increases at a faster rate than across all 6 DOFs together.