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.

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.

Primary Progressive Aphasia (PPA) refers to a group of clinical syndromes caused by a neurodegenerative disease and is categorized into 3 types: APP-S, APP-NF/A, and APP-L. The Grammar Reception Test, in the Brazilian version (TROG2-Br), which focuses on the detection of problems in the Portuguese-Brazilian language, was used in individuals with PPA. The objective of this work is to show the feasibility of using Machine Learning algorithms to classify APP types, based on the individual's performance in the TROG-Br test. For this, the algorithms of Decision Tree, Naive Bayes, kNN, and SVM are applied and evaluated. The results are promising and show that it is possible to classify the types of APP using the individual's performance in the TROG-Br test as predictive attributes.

According to estimates by Alzheimer's Disease International, approximately 1.2 million people in Brazil live with some form of dementia and 100,000 new cases are diagnosed each year. Worldwide, the number reaches 50 million people, and the numbers could reach 74.7 million in 2030 and 131.5 million in 2050 due to an aging population. This scenario shows that the disease characterizes a global health crisis that must be faced. In this context, the article presents the solutions proposed by the academy in recent years according to systematic literature mapping and seeks to encourage scientists and researchers to dedicate efforts to this important topic.

In old age, a series of common health conditions, chronic diseases, and disabilities affect the individual's physical and mental health and prevent the performing of Activities of Daily Living. This paper presents a solution to identify abnormalities in the behavior of the elderly based on ADL (Activities of Daily Living), using Machine Learning, through the Novelty Detection technique. The ADL data were used to create a model that defines the baseline behavior of the elderly, and new observations, to verify significant changes in behavior, are classified as discrepant or abnormal. The Local Outlier Factor, One-class Vector Machine, Robust Covariance, and Isolation Forest novelty detection algorithms were used and evaluated. The model presented reached an accuracy and F1-Score of 96%.