Desenvolvimento de um Estimador para o Movimento do Cotovelo Humano baseado em Inteligência Artificial e Sinais Eletromiográficos

Figueiredo, Rafael; Carvalho, Cristiano; Veslin, E. Y.; Silva, Fabrício Lopes e; Campos, Guilherme Amaral do Prado; Raptopoulos, Luciano Santos Constantin; Bevilácqua, Luiz; Dutra, Max Suell; Andrade, Waltencir

doi:10.26678/abcm.conem2018.con18-1527

Cited by 1 publication

(2 citation statements)

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“…Noninvasive systems have been considered for studies, as current innovations as wireless recordings and robust and portable processing devices allow to implement them in diverse environment conditions. Finally, portable assistive devices based on decoding have been extended to implement EMG signals, as studies that decode continuous elbow angular motion to activate hydraulic systems [51] or virtual devices were found [8].…”

Section: Introductionmentioning

confidence: 99%

“…This method is used to decode a single state in an instant of time t from a set of delayed data. Other studies have been considered the use of artificial neural networks (ANNs) [8,51,52,48] and deep learning [43] to decode motion from biological signals. Finally, the Kalman filter, whose aspects of estimating movement, are examined down below.…”

Section: Introductionmentioning

confidence: 99%

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Development of a closed-loop BMI for elbow movement assistance based on kinematical decoding

Veslin

Dutra

Bevilácqua

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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A closed-loop brain-machine interface for elbow assistance is proposed in this work. The system decodes flexion and extension movements from noninvasive electroencephalographic signals through Kalman filter and uses them to activate a virtual device. A two-degree-of-freedom control scheme drives the model through a decoded path by generating a set of estimated inputs using differential flatness. These inputs are compensated by a feedback loop when decoding errors or external forces actuate the model. The results provide us an insight into the control architecture performance, which is dependent on the decoding precision. These decoding capabilities can be manipulated through a set of parameter configurations, enhancing the path tracking, or decreasing the decoding error according to their values.

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Section: Introductionmentioning

confidence: 99%