n. 27 (2019), Resumos
n. 27 (2019)

Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband

Resumos
Publicado 2019-01-13
Bruno Sgambato
Universidade Estadual de Campinas
Gabriela Castellano
Universidade Estadual de Campinas
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Palavras-chave

Electromyography
Classification
Hand gestures

Como Citar

SGAMBATO, Bruno; CASTELLANO, Gabriela. Identification of hand gestures using pattern recognition of electromyography signals acquired with MyoArmband. Revista dos Trabalhos de Iniciação Científica da UNICAMP, Campinas, SP, n. 27, p. 1–1, 2019. DOI: 10.20396/revpibic2720192283. Disponível em: https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2283. Acesso em: 18 mar. 2026.
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Resumo

The most used way to record hand gestures' information is through the Electromyography (EMG) technique. However, the research in the area is still fragmented. This study aimed at reproducing the high classification performance of hand gestures using EMG data reported in the literature, using a MyoArmband EMG equipment for data acquisition and an LDA classifier, and testing different features and feature selection techniques. The results showed that a performance of 75% is achievable with selected features.

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Referências

Chen, X., & Wang, Z. J. (2013). Pattern recognition of number gestures based on a wireless surface EMG system. Biomedical Signal Processing and Control, 8(2), 184–192.

Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., & Laurillau, Y. (2013). EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications, 40(12), 4832–4840.

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Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

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