Assessment of Web Content Accessibility Levels in Spanish Official Online Education Environments

Diversity-based designing, or the goal of ensuring that web-based information is accessible to as many diverse users as possible, has received growing international acceptance in recent years, with many countries introducing legislation to enforce it. This paper analyses web content accessibility levels in Spanish education portals according to the international guidelines established by the World Wide Web Consortium (W3C) and the Web Accessibility Initiative (WAI). Additionally, it suggests the calculation of an inaccessibility rate as a tool for measuring the degree of non-compliance with WAI Guidelines 2.0 as well as illustrating the significant gap that separates people with disabilities from digital education environments (with a 7.77% average). A total of twenty-one educational web portals with two different web depth levels (42 sampling units) were assessed for this purpose using the automated analysis tool Web Accessibility Test 2.0 (TAW, for its initials in Spanish). The present study reveals a general trend towards non-compliance with the technical accessibility recommendations issued by the W3C-WAI group (97.62% of the websites examined present mistakes in Level A conformance). Furthermore, despite the increasingly high number of legal and regulatory measures about accessibility, their practical application still remains unsatisfactory. A greater level of involvement must be assumed in order to raise awareness and enhance training efforts towards accessibility in the context of collective Information and Communication Technologies (ICTs), since this represents not only a necessity but also an ethical, social, political and legal commitment to be assumed by society.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.