There is a demand for new models of computation intelligence for the recognition of the environment and the obstacles in each moment and the sharing of this information with other users providing temporary dangers notifications, which can enhance blind navigation experience and autonomy. We identified an opportunity to contribute with an integrated strategy to develop a solution to improve the blind autonomy and quality of life. We are looking for solutions to problems that have emerged from the accumulated experience in blind navigation systems research. The main objective of this paper is to present a conceptual model that works based on data obtained from sensors on passive monitoring, worn by bystanders that can combine and correlate the inference patterns that match the obstacles and/or dangers. The model has retro-feedback mechanisms, allowing the sensors to search and pervasively validate the existence of obstacle, ensuring the temporary basis of these risks.
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