Accuracy measures for evaluating computer pointing devices

TLDR

Evaluating computer pointing devices across dynamic tasks is difficult, and existing metrics such as movement time, error rate, and throughput capture only a single measurement per trial, so new performance measures are needed to capture movement behavior during a trial. The paper proposes seven new accuracy measures to detect subtle differences among devices in precision pointing tasks. The seven measures—target re‑entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, and movement offset—are theoretically grounded, computationally defined, and evaluated on four pointing devices. The measures were found to correlate causally with device efficiency (throughput) and to discriminate among devices even when other metrics did not, highlighting their usefulness for pointing device research.

Abstract

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.