Maia Peixoto, HeltonAlves, Erika Costa2023-01-102023-01-102022-12-14ALVES, Erika Costa. Classificação de gestos da mão utilizando sinais sEMG: uma abordagem com tensorflow lite. 2022. 59 f. Orientador: Helton Maia. Trabalho de Conclusão de Curso (Graduação em Engenharia Mecatrônica) - Departamento de Engenharia de Computação e Automação, Universidade Federal do Rio Grande do Norte, Natal, 2022.https://repositorio.ufrn.br/handle/123456789/50899Nowadays, the use of Machine Learning has become essential to automate and facilitate people's lives, and this is no different in the health field. Then, a classifier of hand gestures was studied and implemented based on spectrograms of sEMG signals to be embedded in a low-power microcontroller. For this, a study was carried out on analyzing and processing biological signals, involving digital signal processing and modeling a neural classifier using modern machine learning techniques. In addition, the creation of the model aims at its operation in an embedded form in a low-power microcontroller. In this way, its use in real-time is desired for several applications, and one of them would be to aid the use of hand prostheses built from 3D printers. The good results achieved show the viability of the project.Attribution-NonCommercial 3.0 Brazilhttp://creativecommons.org/licenses/by-nc/3.0/br/Processamento digital de sinaisSinais sEMGMachine learningSistemas embarcadobachelorThesisCNPQ::ENGENHARIAS