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.
With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.
Control and data management in ubiquitous environments is not a trivial activity owing to the heterogeneity of the users, applications and devices, required to exchange information. However, various problems have been found in the literature with regard to privacy information and related to the data used in ubiquitous environments. This paper offers a solution by means of statistical classification algorithms that can be used for control and privacy management. On the basis of the algorithms used in the tests, it proved to be possible to control and manage information by providing definitions of the variables and parameters for users, devices, and ubiquitous environments.
Privacy control and management in ubiquitous environments is not a trivial task. Especially in heterogeneous environments with different criteria and parameters related to communication, devices, users, and features of the environment itself. This work presents a study related to the algorithms that best fit the criteria, parameter, and information for the treatment of data privacy based on the user's history in the ubiquitous environment. For this, a prototype adapted to the UbiPri middleware was developed with the necessary characteristics for the historical control called UbiPri-His. They were tested, identified and identified for the mechanism for the management of data privacy related to the user's usage history, according to the environment and its location. An implementation carried out in a taxonomy, in the UbiPri middleware, and as a solution for comparison and definition of the algorithm with the best performance for the historical data file.
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