Sensory Substitution of Vision: A Systematic Mapping and a Deep Learning Object Detection Proposition Since 1946 methods for sensory substitution of vision has been studied; however, half a century after the beginning of this line of research, this keep been a massive problem in a world with about 50.6 million people with irreversible blindness. This research presents how selfhelp devices for visually impaired are approach in recent years and proposes a new approach based on object recognition with deep learning. Through it, it is possible to perceive the trends in this line of research, how devices obtain information from the environment, how they interact with users, and other aspects xe2x80x94 pointing essential factors to all those who research or wish to study this area.

Assistive Technology through Internet of Things and Edge Computing This paper proposes a framework that applies computer vision and machine learning techniques, through an Internet of Things (IoT) network, with the use of cloud computing for capacity increase. Images are captured by an IoT device and sent for an edge element (IoT node), which processes them, identifies objects, computes distances, and, ultimately, converts that information into audible commands, in order to provide guidance for visuallyimpaired people. Unrecognized objects are sent to a cloud service, in order to provide network retraining and, consequently, system evolution. With the goal of validation, the proposed framework was implemented, using known and proven technologies, such as Raspberry Pi, Esp32CAM, OpenCv, Tensorflow, GoogleTTS and Python. Finally, experimental results demonstrated its feasibility and effectiveness.