Mouse pointing endpoint prediction using kinematic template matching

We present a new method of predicting the endpoints of mouse movements. While prior approaches to endpoint prediction have relied upon normative kinematic laws, regression, or control theory, our approach is straightforward but kinematically rich. Our key insight is to regard the unfolding velocity profile of a pointing movement as a 2-D stroke gesture and to use template matching to predict the endpoint based on prior observed movements. We call our technique kinematic template matching (KTM), which is simple to implement, user-adaptable, and kinematically expressive. In a study of 17 able-bodied participants evaluated over movement amplitudes ranging from 100-800 pixels, we found KTM to predict endpoints that were within 83 pixels of the true endpoint at 50% of the way through the movement, within 48 pixels at 75%, and within 39 pixels at 90%, using 1000 templates per participant. These accuracies make KTM as successful an approach to endpoint prediction as any prior technique, while being easier to implement and understand than most.