HCI for Elderly, Measuring Visual Complexity of Webpages Based on Machine Learning

Abstract

The increasing number of elderly persons, aged 65 and over, highlights the problem of improving their experience with computers and the web considering their preferences and needs. Elderlies' skills like cognitive, haptic, visual, and motor skills are reduced by age. The visual complexity of web pages has a major influence on the quality of user experience of elderly users according to their reduced abilities. Therefore, it is quite beneficial if the visual complexity of web pages could be measured and reduced in applications and websites which are designed for them. In this way a personalized less complex version of the website could be provided for older users. In this article, a new approach for measuring the visual complexity is proposed by using both Human-Computer Interaction (HCI) and machine learning methods. Six features are considered for complexity measurements. Experimental results demonstrated that the trained proposed machine learning approach increases the accuracy of classification of applications and websites based on their visual complexity up to 82% which is more than its competitors. Besides, a feature selection algorithm indicates that features such as clutter and equilibrium were selected to have the most influence on the classification of webpages based on their visual complexity.