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.

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law.

This research discusses the use of Augmented Reality, Virtual Reality and Mixed Reality technology applications in the learning process of relevant content to the Computer Science area. This systematic review aims to identify applications that use technologies to represent virtual environments and support the teaching and learning of Computer Science subjects. A protocol was elaborated and executed, resulting in the final selection of 14 papers from four databases, published from 2010 to 2018. The examined papers presented information that categorized technology applications in terms of tools used. Contents addressed to the identification of applied instructional strategies and techniques, and the recognition of effects on the learning process. As a result, we found virtual environments that show potential to teaching basic content in courses related to Computer Science. In addition, the application of virtual environments in this educational scenario has provided positive effects on the learning process, such as increased interactivity, easier content absorption, increased motivation and interest in the subjects, providing greater understanding and improving efficiency in content transmission.

Outpatient absenteeism is a recurring problem worldwide and in Brazil, it is a chronic problem. The number of appointments and exams scheduled and not performed, due to the non-attendance of patients, reaches significantly high rates and can be seen in all regions in the country and in different types of care and medical specialties. This practice generates waste of resources, disorganizes the offer of services, and limits the guarantee of care at different levels of assistance. In addition, it causes a series of dissatisfactions from users of the health system who really need and have not yet been able to access consultations and exams. This imbalance causes the misuse of the offer, an increase in the queue and waiting time, as well as a financial loss since it is paid for by a professional who is idle due to the absence of patients. It is necessary to understand the profile of these missing patients and to try to discover the reasons that lead this person to be absent, in order to predict a future absence for consultation. Thus, this work presents the study of different machine learning models in order to help predict whether or not the patient will attend the scheduled appointment. As a result, an end-to-end machine learning process was developed, considering exploratory data analysis, pre-processing, creation of machine learning models, analysis of results, and deployment of the most appropriate model in a web application. As a result, it was found that the Decision Tree algorithm represents an interesting choice as a final model for use in future observations.

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.

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.