Full citation
Forsyth, B., MacLean, K. E. (2006). "Predictive Haptic Guidance: Intelligent User Assistance for the Control of Dynamic Tasks." in IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 1, pages 103-113, January/February 2006.
Abstract
Intelligent systems are increasingly able to offer real-time information relevant to a user’s manual control of an interactive
system, such as dynamic system control space constraints for animation control and driving. However, it is difficult to present this
information in a usable manner and other approaches which have employed haptic cues for manual control in “slow” systems often
lead to instabilities in highly dynamic tasks. We present a predictive haptic guidance method based on a look-ahead algorithm, along
with a user evaluation which compares it with other approaches (no guidance and a standard potential-field method) in a 1-DoF
steered path-following scenario. Look-ahead guidance outperformed the other methods in both quantitative performance and
subjective preference across a range of path complexity and visibility and a force analysis demonstrated that it applied smaller and
fewer forces to users. These results (which appear to derive from the predictive guidance’s supporting users in taking earlier and more
subtle corrective action) suggest the potential of predictive methods in aiding manual control of dynamic interactive tasks where
intelligent support is available.
system, such as dynamic system control space constraints for animation control and driving. However, it is difficult to present this
information in a usable manner and other approaches which have employed haptic cues for manual control in “slow” systems often
lead to instabilities in highly dynamic tasks. We present a predictive haptic guidance method based on a look-ahead algorithm, along
with a user evaluation which compares it with other approaches (no guidance and a standard potential-field method) in a 1-DoF
steered path-following scenario. Look-ahead guidance outperformed the other methods in both quantitative performance and
subjective preference across a range of path complexity and visibility and a force analysis demonstrated that it applied smaller and
fewer forces to users. These results (which appear to derive from the predictive guidance’s supporting users in taking earlier and more
subtle corrective action) suggest the potential of predictive methods in aiding manual control of dynamic interactive tasks where
intelligent support is available.
SPIN Authors
Year Published
2006