Accessible Interface for Context Awareness in Mobile Devices for Users With Memory Impairment

Abstract

Memory problems usually appear because of aging or may happen because of a brain injury. Such problems prevent the person from performing daily activities. In this paper, the authors propose a framework to develop a smartphone solution to detect and recognize the user context. In order to build the context detection framework, the authors compare three different machine learning techniques (C.4.5, random, and BFTree) in terms of context detection accuracy. Then, the authors use the classification technique with the highest accuracy in a mobile application to help users by detecting their context. The authors develop two interfaces based on the suggested accessibility features for users with memory impairment. Two scenarios are used to evaluate the user interface, and the results prove the applicability and the usability of the proposed context detection framework.