Study on the emg-based grasp gesture classification using deep learning and application to active prosthetics

被引：3

作者：

Jo Y.U. ^{[1
]}

Oh D.C. ^{[1
]}

机构：

[1] Department of Biomedical Engineering, Konyang University

来源：

Journal of Institute of Control, Robotics and Systems | 2019年 / 25卷 / 03期

关键词：

Active prosthetics; CNN; Hand gesture of grasping; Robot hands; SEMG sensor;

D O I：

10.5302/J.ICROS.2019.19.8002

中图分类号：

学科分类号：

摘要：

In this paper, we extract the learning and test data for the "hand gesture of grasping" through the sEMG sensor, execute the Deep Learning CNN (convolutional neural network) algorithm by appropriately modifying it, and classify typical hand gestures that catch objects with a classification success rate (accuracy) of approximately 93.8%. In addition, we have constructed a system that can operate robot hands in real time from these classified commands to make active prosthetics. © ICROS 2019.

引用

页码：229 / 234

页数：5

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