Implicit detection of user handedness in touchscreen devices through interaction analysis

Author:

Fernández Carla1,Gonzalez-Rodriguez Martin1,Fernandez-Lanvin Daniel1,De Andrés Javier2,Labrador Miguel3

Affiliation:

1. Department of Computer Science, University of Oviedo, Oviedo, Asturias, Spain

2. Department of Accounting, University of Oviedo, Oviedo, Asturias, Spain

3. Department of Computer Science, University of South Florida, Tampa, FL, United States of America

Abstract

Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user’s handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.

Funder

The Department of Science, Innovation, and Universities (Spain) under the National Program for Research, Development, and Innovation

The National Science Foundation

REU Site on Ubiquitous Sensing

Publisher

PeerJ

Subject

General Computer Science

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