Force Estimation Based on sEMG using Wavelet Analysis and Neural Network

被引：0

作者：

Jiang, Du ^{[1
]}

Li, Gongfa ^{[1
]}

Jiang, Guozhang ^{[2
]}

Chen, Disi ^{[3
]}

Ju, Zhaojie ^{[3
]}

机构：

[1] Wuhan Univ Sci & Technol, Minist Educ, Key Lab Met Equipment & Control Technol, Wuhan, Peoples R China

[2] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan, Peoples R China

[3] Univ Portsmouth, Sch Comp, Portsmouth, Hants, England

来源：

2019 9TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST2019) | 2019年

关键词：

sEMG; Wavelet packet analysis; BP neural network; LSTM; RECOGNITION; SIGNALS;

D O I：

10.1109/icist.2019.8836897

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

for further improving the action of sEMG in human-robot interaction, wavelet and neural network is utilized in prediction of grip force. Firstly, it is described based on the introduction platform how to gain sEMG as well as its traditional features. Then, the wavelet decomposition and reconstruction algorithm is used to analyze the sEMG signals and extract the corresponding energy characteristics. Different grasp force of and sEMG signals are collected simultaneously whose feature matrix is used in training model. Those are evaluated by root mean square error, whose results show that RMSE = 1.0 +/- 0.4 of BP network and RMSE = 1.8 +/- 0.5 of LSTM model.

引用

页码：320 / 326

页数：7

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