This paper is concerned with simple human performance “laws of action ” for three classes of tasks – pointing, crossing, and steering, as well as their applications in virtual reality research. In comparison to Fitts ’ law of pointing, the “law of steering ” – the quantitative relationship between human temporal performance and the movement path’s spatial characteristics – has been notably under investigated. After a historical review of research on the law of steering in different domains and time periods, we examine the applicability of the law of steering in a VR locomotion task. Participants drove a virtual vehicle in a virtual environment on paths whose shape and width were systematically manipulated. Results showed that the law of steering indeed applies to locomotion in virtual environments. Participants’ mean trial completion times linearly correlated (r² between 0.985 and 0.999) with an index of difficulty quantified as path distance to width ratio for the straight and circular paths used in this experiment. Their average mean and maximum speed was linearly proportional to path width. Such human performance regularity provides a quantitative tool for 3D human machine interface design and evaluation. We also propose to use the law of steering model in VR manipulation tasks such as the “ring and wire” task in the future.

This paper introduces a fast decision tree based feature classification system for hand gesture recognition and pose estimation. Training of the decision trees is performed using synthetic data and classification is performed on images of real hands. The presence of each finger is individually classified and gesture classification is performed by parts. The attributes used for training and classification are simple ratios between the foreground and background pixels of the hand silhouette. The system does not require the hand to be perfectly aligned to the camera or use any special markers or input gloves on the hand.