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Selection of Grasping Target and Control System of Robotic Prosthetic Hand using Images and Deep Learning
Year of publication
2020
Author
Haejune Park, Bohyeon An, Junmin Baek, Dongkyu Lee, Changwon Kim, Subin Joo, Ohwon Kwon, Min Young Kim, Joonho Seo
Journal
Journal of Institute of Control, Robotics and Systems
volume
1976-5622
Issue
5
Page
312-317
File
첨부 Selection of Grasping Target and Control System of Robotic Prosthetic Hand using Images and Deep Learning.pdf (2.4M) 0회 다운로드 DATE : 2020-09-11 11:24:01

 Robotic prosthetic hands are a device that helps to improve the quality of life for patients without hands. Recently, robotic prosthetic hands can perform various grasping patterns because of improvement of bioengineering and robotics. The research that automatically selects the appropriate operation according to the situation is important. Many previous studies have used EMG signals. However, EMG signals are difficult to generalize because EMG signals vary depending on the position of the muscle. In this study, we developed a system for controlling robotic prosthetic hands using images and deep learning to facilitate generalization. We also proposed a method for selecting a grasping target to be held in the image. These results will help to improve the quality of life of the robotic prosthetic hand user.



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