Force estimation based on sEMG using wavelet analysis and neural network

Du Jiang; Gongfa Li; Guozhang Jiang; Disi Chen; Zhaojie Ju

doi:10.1109/ICIST.2019.8836897

Force estimation based on sEMG using wavelet analysis and neural network

Du Jiang, Gongfa Li, Guozhang Jiang, Disi Chen, Zhaojie Ju

School of Computing

Wuhan University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

154 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	2019 9th International Conference on Information Science and Technology (ICIST)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	320-326
Number of pages	7
ISBN (Electronic)	978-1-7281-2106-2
ISBN (Print)	978-1-7281-2107-9
DOIs	https://doi.org/10.1109/ICIST.2019.8836897
Publication status	Published - 16 Sep 2019
Event	2019 9th International Conference on Information Science and Technology - Hulunbuir, China Duration: 2 Aug 2019 → 5 Aug 2019

Publication series

Name	9th International Conference on Information Science and Technology (ICIST)
Publisher	IEEE
ISSN (Print)	2573-3311

Conference

Conference	2019 9th International Conference on Information Science and Technology
Country/Territory	China
City	Hulunbuir
Period	2/08/19 → 5/08/19

Access to Document

10.1109/ICIST.2019.8836897

ICIST_2019_paper_108_Force_Estimation
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Accepted author manuscript (Post-print), 710 KB

Cite this

Jiang, D., Li, G., Jiang, G., Chen, D., & Ju, Z. (2019). Force estimation based on sEMG using wavelet analysis and neural network. In 2019 9th International Conference on Information Science and Technology (ICIST) (pp. 320-326). (9th International Conference on Information Science and Technology (ICIST)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIST.2019.8836897

@inproceedings{d4c1da2d0b5c4b3492adb76d7b7e7334,

title = "Force estimation based on sEMG using wavelet analysis and neural network",

abstract = "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.",

author = "Du Jiang and Gongfa Li and Guozhang Jiang and Disi Chen and Zhaojie Ju",

year = "2019",

month = sep,

day = "16",

doi = "10.1109/ICIST.2019.8836897",

language = "English",

isbn = "978-1-7281-2107-9",

series = "9th International Conference on Information Science and Technology (ICIST)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "320--326",

booktitle = "2019 9th International Conference on Information Science and Technology (ICIST)",

address = "United States",

note = "2019 9th International Conference on Information Science and Technology ; Conference date: 02-08-2019 Through 05-08-2019",

}

Jiang, D, Li, G, Jiang, G, Chen, D & Ju, Z 2019, Force estimation based on sEMG using wavelet analysis and neural network. in 2019 9th International Conference on Information Science and Technology (ICIST). 9th International Conference on Information Science and Technology (ICIST), Institute of Electrical and Electronics Engineers Inc., pp. 320-326, 2019 9th International Conference on Information Science and Technology, Hulunbuir, China, 2/08/19. https://doi.org/10.1109/ICIST.2019.8836897

Force estimation based on sEMG using wavelet analysis and neural network. / Jiang, Du; Li, Gongfa; Jiang, Guozhang et al.

2019 9th International Conference on Information Science and Technology (ICIST). Institute of Electrical and Electronics Engineers Inc., 2019. p. 320-326 (9th International Conference on Information Science and Technology (ICIST)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Force estimation based on sEMG using wavelet analysis and neural network

AU - Jiang, Du

AU - Li, Gongfa

AU - Jiang, Guozhang

AU - Chen, Disi

AU - Ju, Zhaojie

PY - 2019/9/16

Y1 - 2019/9/16

N2 - 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.

AB - 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.

U2 - 10.1109/ICIST.2019.8836897

DO - 10.1109/ICIST.2019.8836897

M3 - Conference contribution

SN - 978-1-7281-2107-9

T3 - 9th International Conference on Information Science and Technology (ICIST)

SP - 320

EP - 326

BT - 2019 9th International Conference on Information Science and Technology (ICIST)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 9th International Conference on Information Science and Technology

Y2 - 2 August 2019 through 5 August 2019

ER -

Jiang D, Li G, Jiang G, Chen D, Ju Z. Force estimation based on sEMG using wavelet analysis and neural network. In 2019 9th International Conference on Information Science and Technology (ICIST). Institute of Electrical and Electronics Engineers Inc. 2019. p. 320-326. (9th International Conference on Information Science and Technology (ICIST)). doi: 10.1109/ICIST.2019.8836897

Force estimation based on sEMG using wavelet analysis and neural network

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