Adaptations of Neuroprostheses for Training: A Technological and Systematic Review

Abstract

Certain medical conditions, genetic disabilities, and/or accidents limit certain parts of the body, such as the upper and/or lower extremities. This has seen technological advancement and has provided the development of various robotic devices (Neuroprosthetics) to assist in the rehabilitation process. Likewise, the use of Neural Networks, Supervised Machine Learning, or motion testing has the purpose of training the device itself to connect through sensors that capture biological signals to be pre-processed in controllers to target the movement to be performed. This research aims to analyze the characteristics of the components, control, and type of training for developing an efficient Neuroprosthesis. As a result of this research, different databases and search engines were used to collect information from various research from 2020 to 2023. Finally, it is concluded that the study aims to provide the most relevant information possible in the field of Neuroprosthesis concerning its training to be used in future research works and to be able to build and/or adapt a device with better characteristics for daily activities of the user and in the rehabilitation process.