In view of the difficulties in evaluating computer pointing devices across different tasks within dynamic and complex systems, new performance measures are needed. This paper proposes seven new accuracy measures to elicit (sometimes subtle) differences among devices in precision pointing tasks. The measures are target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, and movement offset. Unlike movement time, error rate, and throughput, which are based on a single measurement per trial, the new measures capture aspects of movement behaviour during a trial. The theoretical basis and computational techniques for the measures are described, with examples given. An evaluation with four pointing devices was conducted to validate the measures. A causal relationship to pointing device efficiency (viz. throughput) was found, as was an ability to discriminate among devices in situations where differences did not otherwise appear. Implications for pointing device research are discussed.

A joystick text entry method for game controllers and mobile phones would be valuable, since these devices often have joysticks but no conventional keyboards. But prevalent joystick text entry methods are slow because they are selection-based. EdgeWrite, a new joystick text entry method, is not based on selection but on gestures from a unistroke alphabet. Our experiment shows that this new method is faster, leaves fewer errors, and is more satisfying than date stamp and selection keyboard (two prevalent selection-based methods) for novices after minimal practice. For more practiced users, our results show that EdgeWrite is at least 1.5 times faster than selection keyboard, and 2.4 times faster than date stamp.

NEC Foundation of America, NISH, Synaptics, and A.T. Sciences.