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.

We present a model for predicting expert text entry rates for several input methods on a 12-key mobile phone keypad. The model includes a movement component based on Fitts' law and a linguistic component based on digraph, or letter-pair, probabilities. Predictions are provided for one-handed thumb and two-handed index finger input. For the traditional multi-press method or the lesser-used two-key method, predicted expert rates vary from about 21 to 27 words per minute (wpm). The relatively new T9 method works with a disambiguating algorithm and inputs each character with a single key press. Predicted expert rates vary from 41 wpm for one-handed thumb input to 46 wpm for two-handed index finger input. These figures are degraded somewhat depending on the user's strategy in coping with less-than-perfect disambiguation. Analyses of these strategies are presented.