Identification of electromyographic signals using machine learning techniques and low-cost technologies
DOI:
https://doi.org/10.47734/regrasp.v10.03.p18-31Keywords:
Electromyographic Signals, Gesture Recognition, Machine Learning, Human-Robot InterfaceAbstract
A interface humano-robô (IHR) é um tópico de pesquisa muito estudado recentemente. Este tópico se trata da aquisição, processamento e interpretação de sinais eletrobiológicos provenientes de diferentes partes do corpo humano e aplicação desses sinais para o controle de sistemas robóticos. A IHR torna-se necessária em aplicações para pessoas com deficiências, profissionais que trabalham em ambientes perigosos ou até mesmo na cirurgia robótica. O enfoque deste trabalho é a identificação de sinais eletromiográficos (EMGs). Sensores EMGs foram colocados em regiões específicas do braço de uma pessoa e a identificação gestual foi realizada. Inicialmente, foram determinados os sensores EMGs e o microcontrolador utilizando tecnologias de baixo custo. Posteriormente, foram aplicadas técnicas de aprendizado de máquina para identificação gestual. As principais contribuições do trabalho foram a utilização de um sensor EMG disponível comercialmente no Brasil e em diversos países, de fácil acessibilidade e pouco explorado na literatura, além da utilização dos aplicativos de extração de características (features) e técnicas de aprendizado de máquina do software Matlab, que são ferramentas práticas, eficientes, e também pouco exploradas na literatura. O modelo de aprendizado de máquina obtido foi bastante preciso e pode ser aplicado futuramente para o controle de sistemas robóticos.
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