An Adaptive, Comprehensive Application to Support Home-Based Visual Training for Children With Low Vision

• Main Authors: Yolanda Matas, Felix Hernandez-Del-Olmo, Elena Gaudioso, Carlos Santos
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Educational technology; computer applications; adaptive systems; low vision
• Online Access: https://ieeexplore.ieee.org/document/8908669/

Low vision is a visual impairment that cannot be improved by standard vision aids such as glasses. Therefore, to improve their visual skills, people affected by low vision usually follow a visual training program planned and supervised by an expert in this field. Visual training is especially suitable for children because of their plasticity for learning. However, due to a lack of specialists, training sessions are usually less frequent than optimal. Thus, home-based visual training has emerged as a solution to this problem because it can be undertaken by experts and families together. We implemented the Visual Stimulation on the Internet (EVIN) application, which provides comprehensive visual training tasks through games. It also provides reports on children's performance in these visual training tasks. Although EVIN has shown its usefulness in previous works, two main solutions are needed: (i) a support setup to help experts and families work together to address, among other things, the large variety of exercises and different configurations that can be prescribed and (ii) a rigorous experimental design to compare children trained with EVIN and those trained with traditional materials. To face these challenges, we present an adaptive version of EVIN that provides a new design tool that allows experts to plan visual training tasks through templates in advance. In addition, we developed new metrics and reports to achieve a more accurate assessment of a child's improvement. Among other results, it allowed us to develop an reliable experiment to evaluate significant improvements in children trained with EVIN.