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Development of Behavior Profile of Users with Visual Impairment
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Development of Behavior Profile of Users with Visual Impairment
Journal
Advances in Intelligent Systems and Computing
Date Issued
2020
Author(s)
Guevara Maldonado, César Byron
Centro de investigación en Mecatrónica y Sistemas Interactivos
Arias Flores, Hugo Patricio
Centro de investigación en Mecatrónica y Sistemas Interactivos
Varela Aldas, José
Centro de Investigación de Ciencias Humanas y de la Educación
Castillo Salazar, David Ricardo
Facultad de Ciencias de la Educación
Borja M.
Fierro-Saltos W.
Rivera R.
Hidalgo-Guijarro J.
Yandún-Velasteguí M.
Type
Conference Paper
DOI
10.1007/978-3-030-27928-8_75
URL
https://cris.indoamerica.edu.ec/handle/123456789/8932
Abstract
The interaction of the user with visual impairment with assistive technologies, and in particular with screen readers, generates a group of actions and events during their navigation. These interactions are defined as behavioral patterns, which have a sequence that occurs at specific time slot. Understanding user behavior by analyzing their interaction with applications, in addition, details the characteristics, relationships, structures and functions of the sequence of actions in a specific application domain. The objective of this document is to find activity patterns from a set of commands used by the user, combining data mining and a Bayesian model. This model calculates the probability of the functions used with the screen reader and generates a behavior profile to improve the user experience. For this study, the screen reader JAWS version 2018, the Open Journal Systems platform version 3.0.1 and a computer with Windows 10 operating system were used. During the first phase, command history used by the user by interacting with the Open Journal Systems platform were collected. The result is that the accessibility of users with visual impairment to interact with the computer and its applications has been improved by applying this model. © Springer Nature Switzerland AG 2020.
Subjects
Autonomous learning a...
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Dec 6, 2024
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