Real-Time Operating Hand Detection for the Optimization of Mobile Web Interfaces

Mobile devices now rival desktop computers as a method of web browsing. Even so, many web applications are still designed with the desktop setting in mind. As screen sizes of mobile devices continue to get larger, operating smartphones single-handedly becomes increasingly difficult. This paper explores the possibility of automatic operating hand detection by capturing users' touch traces during normal browsing activities. Automatic operating hand detection would enable web applications to adapt their interfaces and better suit the user's laterality. Supervised classifiers were constructed for the primary goal of operating hand detection (left, right), but also to detect hand posture (thumb, index). Both classifiers use features extracted from the touch traces and button clicks on a dataset of 174 users. The resulting classifiers featured true positive rates of 96.0% and 70.1% respectively when tested using 10-fold cross-validation. While previous studies achieved similarly accurate results for operating hand (94.1%) and posture (82.6%) detection, the approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers.