UAware Alert: uma plataforma sensível ao contexto para alertas antecipados acessíveis.

Abstract

CONTEXTUALIZATION: Natural disasters lead to damage and loss of life around the world. Early Warning Systems, are systems that generate alerts, in advance, to warn the population about these events. Prediction institutions send information about events that can cause disasters in a standard file known as Common Alerting Protocol (CAP). In general, an EWS receives a CAP file, extracts its data, and sends warnings to the population through communication channels such as SMS, e-mail, mobile applications, and others. Most EWS does not consider the specific needs of vulnerable groups and send the same alert to everyone, whether they are people with disabilities (e.g. deaf people, blind people etc.) or are in an area with higher risk for the event in question. OBJECTIVES: This study's purpose is to create an EWS to send warns to people, considering people with disabilities, who are in risk areas. METHOD: This research was carried out through a combination approach of the following methods: Systematic Mapping Study, exploratory studies, and empirical studies. Exploratory studies, through semi-structured interviews, served to achieve more familiarity with the research problem. Based on these studies, a context model, behavioral rules, and an automated process for creating an accessible resources library were proposed, in order to enable the development of an EWS that sends alerts to different groups of users. Finally, the evaluation was carried out from two perspectives: (i) evaluating whether the alerts are delivered to the expected recipients and with the expected personalization; (ii) evaluating the context model and behavioral rules. For evaluation (i) an empirical approach was used through quantitative techniques. For evaluation (ii), a survey was conducted with Civil Defense experts from the states of Brazil. RESULTS: This study presents an architecture and a context model with contextual rules for an EWS that considers people from vulnerable groups (for example deaf or blind people). Finally, it presents an implemented and functional prototype, the UAware Alert, with a management interface for sending alerts and a mobile app for receiving the warning in an accessible way. CONCLUSION: EWS uses CAP messages and the person's location to send alerts. But to alert people who have different needs, it is also necessary to consider the profile of the person who will receive the alert and their location. Thus, the context model, behavioral rules, and media generation process of this study enables the development of an EWS, which sends instructions focused on the region where the person is located, either in text or in formats accessible to the needs of each user.