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Four methods for estimating the recruitment curve of isometric, electrically stimulated muscle are described. Three of the methods were tested experimentally in isolated tibialis anterior and medial gastrocnemius muscles of cats. The three methods are steady-state step response, peak impulse response, and deconvolved ramp response. The fourth method, described but not tested, is a stochastic iteration technique. The results demonstrate that estimations of recruitment curves depend on the method used and that all methods are sensitive to short-term and long-term time-variations in muscle properties. While the step response technique is the traditional method for estimating recruitment curves, the ramp deconvolution method appears to offer acceptable accuracy with much shorter testing times.

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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.