An Application of Machine Learning for Police Mobile Computer Terminal Usability Evaluation

Abstract

Police in-vehicle technologies especially mobile computer terminals (MCTs) are the major cause of motor vehicle crashes for law enforcement officers. Previous studies have found several usability issues with the design of MCT interfaces. The objective of this study was to develop an algorithm for classification of MCT interface usability based on a combination of officer performance, visual attention allocation, and subjective measures. It was found that speed deviation, secondary task completion time, off-road fixation frequency and glance duration are informative features that associate with MCT usability. The developed algorithm has the potential to be used in MCT design and development process to ensure efficient interaction in multi-tasking situations of driving and using the MCT.

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