Open AccessAdaptive Fitts for Adaptive Interface
Author(s) -
Saad Hasan
Publication year - 2018
Publication title -
the aiub journal of science and engineering
Language(s) - English
Resource type - Journals
eISSN - 2520-4890
pISSN - 1608-3679
DOI - 10.53799/ajse.v17i2.9
Subject(s) - computer science , interface (matter) , fitts's law , human–computer interaction , task (project management) , user interface , visualization , point (geometry) , logarithm , artificial intelligence , machine learning , engineering , mathematical analysis , geometry , mathematics , systems engineering , bubble , maximum bubble pressure method , parallel computing , operating system
Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.