A Dataset and Post-Processing Method for Pointing Device Human-Machine Interface Evaluation

Abstract

The evaluation of human-machine interfaces (HMI) requires quantitative metrics to define the ability of a person to effectively achieve their goals using the HMI. In particular, for pointing-device type HMIs such as the computer mouse, an experiment quantifying movement by performing repetitive target selections allows defining a useful metric known as throughput (TP) using the Fitts’ Law test. In this work, a dataset obtained from an automated protocol application is presented, which is made publicly available through an on-line platform. A post-processing method to obtain performance parameters from the dataset is also presented, and its output is used to validate the data against similar experiments in the literature.