Identification of electromyographic signals using machine learning techniques and low-cost technologies

Jhenifer July Sousa De Almeida; Caio Igor Gonçalves Chinelato

doi:10.47734/regrasp.v10i3.1247

Jhenifer July Sousa De Almeida IFSP/Campus São Paulo
Caio Igor Gonçalves Chinelato Federal University of ABC (UFABC) - Center of Engineering https://orcid.org/0000-0001-8227-2541

DOI: https://doi.org/10.47734/regrasp.v10i3.1247

Palavras-chave: Sinais Eletromiográficos, Reconhecimento Gestual, Aprendizado de Máquina, Interface Humano-Robô

Resumo

The human-robot interface (HRI) has recently become a widely studied research topic. This topic addresses the acquisition, processing and interpretation of electrobiological signals from different parts of the human body and the application of these signals for the control of robotic systems. The HRI is essential for applications involving people with disabilities, professionals working in hazardous environments, and even robotic surgery. The focus of this work is the identification of electromyographic (EMG) signals. EMG sensors were placed on specific regions of a person’s arm, and gesture recognition was performed. Initially, the EMG sensors and microcontroller were determined using low-cost technologies. Posteriorly, machine learning techniques were applied for gesture recognition. The main contributions of the work were the use of an EMG sensor commercially available in Brazil and in several countries, easily accessible and little explored in the literature, besides the use of feature extraction and machine learning techniques applications from the Matlab software, which are practical and efficient tools, and also little explored in the literature. The resulting machine learning model was quite accurate and can be applied in the future for the control of robotic systems.

Biografia do Autor

Jhenifer July Sousa De Almeida, IFSP/Campus São Paulo

Undergraduate Student in Production Engineering - IFSP/Campus São Paulo

Caio Igor Gonçalves Chinelato, Federal University of ABC (UFABC) - Center of Engineering

Professor and researcher at Federal University of ABC (UFABC) - Center of Engineering, Modeling and Applied Social Sciences (CECS), Santo André, Brazil. Mechatronics Technician graduated from ETEC Jorge Street, São Caetano do Sul, Brazil. B.Sc. Degree in Science and Technology, B.Sc. Degree in Instrumentation, Automation and Robotics Engineering, and M.Sc. Degree in Mechanical Engineering from the UFABC. Ph.D. Degree in Electrical Engineering from the Polytechnic School of University of São Paulo (EPUSP), São Paulo, Brazil. The research interests include Robotics, Control Systems, Mechatronics, Automation, and Engineering Education.

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