基于Semg和CNN-GRU的机械手手势识别与主从控制

Zhaojie Ge, Zhile Wu, Xu Han, Ping Zhao
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引用次数: 3

摘要

肌表电信号是伴随肌肉收缩的生物电信号。对于主从操作场景,如假手患者,可以收集其上肢肌电信号,并与患者的手势意图相对应。因此,使用与表面肌电信号识别模块集成的从机械手,主方可以控制其做出手势,满足其日常生活的需要。本文基于表面肌电信号和深度学习进行了手势识别,实现了机械手的主从控制。根据训练结果,可以得到具有最高准确率的手势分类识别网络模型。然后结合集成机械手,将手势所对应的机械手的控制信号发送到机械手的控制模块。最后搭建了一个原型系统,利用表面肌电信号实现了机械手的主从控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gesture Recognition and Master-slave Control of a Manipulator Based On Semg and CNN-GRU
Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For masterslave manipulation scenario such as patients with prosthetic hands, their upper limb sEMG signals can be collected and corresponded to the patient' s gesture intention. Therefore, using a slave manipulator that integrated with the sEMG signal recognition module, the master side could control it to make gestures and meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. According to the results of training, the network model with the highest accuracy of gesture classification and recognition can be obtained. Then, combined with the integrated manipulator, the control signal of the manipulator corresponding to the gesture is sent to the control module of the manipulator. In the end, a prototype system is built and the master-slave control of the manipulator using the sEMG signal is realized.
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