According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

Abstract. This paper describes the development of Where do I Go, a Mobile Tourist Recommendations System that uses recommendation of interest points according to user profile, temporal and semantic constraints, using Case Based Reasoning (CBR). The application aims to make recommendations to tourists during the experimentation of the city, which are in accordance with their tourism preferences and the context at the time of recommendation. CBR stores knowledge around a particular domain in case format, where each case has a problem part and another solution. CBR is premised on the fact that similar problems have similar solutions, where the basis for solving new problems is previously solved problems.

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According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by IoT devices and applies Artificial Intelligence models, specifically Machine Learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims at identifying the Machine Learning models used in multiple different research about Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, IoT devices used, and gaps and opportunities for further development. Survey results show that 50% of the analyzed research address visual impairment, and for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constituted the majority of IoT devices. Deep Neural Networks represent 81% of the Machine Learning models applied in the reviewed research.

O turismo é um setor cada vez mais relevante do ponto de vista cultural e com grande impacto econômico. Ferramentas que auxiliem o turista a ter uma melhor experiência no local visitado são cada vez mais solicitadas. Neste contexto, tem-se os Sistemas de Recomendação de Turismo. Tais sistemas servem de apoio a tomada de decisão e personalização de conteúdo, com base nas necessidades e preferências dos viajantes. O grande problema são as informações desatualizadas sobre os pontos turísticos. Neste sentido, este trabalho apresenta um sistema de recomendações turísticas que utiliza raciocínio baseado em casos e informações sobre geolocalização para apresentar aos usuários os pontos turísticos mais pertinentes ao seu perfil e com a localização atualizada.