Adaptive Fitts for Adaptive Interface

Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.

Single Feedback Model of Human Goal-Directed Movement

Two prominent models frequently used to explain targeted human movement are the stochastic optimized-submovement model and the minimum variance model. Both successfully explain the speed-accuracy tradeoff known as Fitts’ law, but neither is complete. The former cannot predict movement trajectory between the endpoints, while the latter is not congruent with the multiple movement segments often observed in human motion. In this paper, a new model is proposed in which an aimed movement consists of two submovements and a single feedback instant, with the trajectory of each submovement being individually optimized. Simulations using the proposed model show that the optimal transition between two submovements occurs at an early stage of the movement, and produces a sharp peak in the acceleration profile. This result is consistent with psychophysical data. Also observed in numerical simulation is the bell-shaped positional variance curve that is in agreement with psychophysical data.