PPGINF PÓS-GRADUAÇÃO EM ENGENHARIA DA INFORMAÇÃO FUNDAÇÃO UNIVERSIDADE FEDERAL DO ABC Phone: Not available http://propg.ufabc.edu.br/ppginfo

Banca de DEFESA: CAIO LIMA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : CAIO LIMA
DATE: 22/12/2022
TIME: 09:00
LOCAL: https://conferenciaweb.rnp.br/webconf/filipe-12
TITLE:

Arm and Forearm Prosthesis Simulator Using Electromyographic Signals


PAGES: 94
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

Approximately 7\% of the Brazilian population has some motor disability, and this public is generally more socially vulnerable. In order to mitigate these impacts, assistive technologies such as upper limb prostheses have been developed, especially those that use surface electromyographic signals (\textit{sEMG}). Although several of these prostheses have a high success rate (above 90\% in some cases), they have a very high average cost, in the order of \$50 thousand dollars. In order to improve the use of existing prostheses, reducing their development complexity and thus seeking to reduce the prices of these devices, simulation tools have been developed. Based on these objectives, an open source simulator controlled by \textit{sEMG} signals was developed, which seeks in a practical and economical way to capture and process these signals and their respective positions (separated by categories) and, in addition, perform the simulation of upper limb prostheses and robotic arms, adequately representing elbow flexion and extension movements. For this task, the simulator was divided into three modules, the first being the \textit{sEMG} signal acquisition module together with the capture of the elbow flexion and extension angle, using a common \textit{Webcam} for recording the elbow extension and flexion angles and the device \textit{Myo}, from the company Thalmic Labs to obtain the signals \textit{sEMG}. The second module is responsible for training the \textit{sEMG} signals and generating classifiers, using the Python language, together with the \textit{scikit-learn} library, and the third module, which performs the simulation of the robotic arm, using the simulation software \textit{Gazebo}, together with the control software \textit{ROS 2}.\cmmnt{\textbf{Results:}} These modules provided tools that allow flexibility in capturing signals \textit{EMG}, visualization, training, and design of classifiers, as well as a simple graphical interface for modeling and simulation of robotic arms.


BANKING MEMBERS:
Presidente - Interno ao Programa - 2090028 - FILIPE IEDA FAZANARO
Membro Titular - Examinador(a) Externo ao Programa - 3296711 - THIAGO BULHOES DA SILVA COSTA
Membro Titular - Examinador(a) Externo ao Programa - 2123666 - FERNANDO SILVA DE MOURA
Membro Suplente - Examinador(a) Interno ao Programa - 2356637 - KENJI NOSE FILHO
Membro Suplente - Examinador(a) Externo à Instituição - RAFAEL FERRARI - UNICAMP
Membro Suplente - Examinador(a) Externo à Instituição - LEVY BOCCATO - UNICAMP
Notícia cadastrada em: 23/11/2022 13:48
SIGAA | UFABC - Núcleo de Tecnologia da Informação - ||||| | Copyright © 2006-2022 - UFRN - sigaa-1.ufabc.int.br.sigaa-1-prod