Human motion models for reaching and pointing tasks are used by designers of software, cellphones, dashboards, cockpits, assembly lines and many other human interfaces. Fitts' Law specifies a logarithmic two-parameter relationship between motion duration and the ratio of target distance over target size. This paper presents two large open-access datasets from experimental user studies, first a controlled (in-lab) study that collected 16,170 trajectory time measurements from 46 participants, and second an uncontrolled (web-based) study that collected 78,410 trajectory time measurements from an uncertain number of participants who used a web-based java applet. Variants of Fitts' Law use square root and modified logarithmic functions; we present a succinct derivation of the square-root variant based on optimal control theory. Using root-mean-square error (RMSE), we compare three two-parameter models that relate motion duration to the ratio of target distance over target size: LOG (Fitts' original logarithmic function), SQR (squareroot), and LOG' (McKenzie's logarithmic plus 1.0) [1]. We find:(1) the data from the controlled and uncontrolled studies are remarkably consistent; (2) for homogeneous targets (with fixed size and distance), the SQR model yields a significantly better fit than LOG or LOG', except with the most difficult targets (where the ratio of target distance over target size is large) where the models are not significantly different; and (3) for heterogeneous targets (with varying size and distance), SQR yields a significantly better fit than LOG for easy targets and LOG yields a significantly better fit for targets of medium difficulty, while the LOG' model yields a significantly better fit than both LOG and SQR on very difficult targets. The anonymized datasets of 94, 580 human reaching motion timing measurements are, to our knowledge, the largest collected to date
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