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http://dx.doi.org/10.7746/jkros.2017.12.2.206

Wrist and Grasping Forces Estimation using Electromyography for Robotic Prosthesis  

Kim, Young-Jin (Korea institute of Industrial Technology)
Lee, Dong-Hyuk (Korea institute of Industrial Technology)
Park, Hyeonjun (Korea institute of Industrial Technology)
Park, Jae-Han (Korea institute of Industrial Technology)
Bae, Ji-Hun (Korea institute of Industrial Technology)
Baeg, Moon-Hong (Korea institute of Industrial Technology)
Publication Information
The Journal of Korea Robotics Society / v.12, no.2, 2017 , pp. 206-216 More about this Journal
Abstract
This paper proposes a method to simultaneously estimate two degrees of freedom in wrist forces (extension - flexion, adduction - abduction) and one degree of freedom in grasping forces using Electromyography (EMG) signals of the forearms. To correlate the EMG signals with the forces, we applied a multi - layer perceptron(MLP), which is a machine learning method, and used the characteristics of the muscles constituting the forearm to generate learning data. Through the experiments, the similarity between the MLP target value and the estimated value was investigated by applying the coefficient of determination ($R^2$) and root mean square error (RMSE) to evaluate the performance of the proposed method. As a result, the $R^2$ values with respect to the wrist flexion-extension, adduction - abduction and grasping forces were 0.79, 0.73 and 0.78 and RMSE were 0.12, 0.17, 0.13 respectively.
Keywords
Robot prosthesis; Electromyography; Multilayer perceptron; Force estimation;
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Times Cited By KSCI : 1  (Citation Analysis)
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