research-article

KUKA Real-Time Control through Angle Estimation of Wrist from sEMG with Support Vector Regression

Authors:

Xiaorong Feng,

Qingsong Ai,

Wei MengAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 389 - 393

https://doi.org/10.1145/3297156.3297240

Published: 08 December 2018 Publication History

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Abstract

In this paper, a wrist joint angle estimation model based on support vector regression(SVR) is established, which is optimized by combining the cuckoo algorithm with the steepest descent method. The sEMG signals were sampled from two forearm muscles. In order to shorten the model train time and improve the accuracy of the estimation model, a new technique combining cuckoo algorithm with the steepest descent optimization method is proposed to find the bestc and bestg of SVR in a short time. Experiments on a subject showed, the proposed method shows a good performance in both accuracy and timeliness. Then, in order to verify the estimation effectiveness and practicality of sEMG, the estimation angle was used to control a KUKA robot in real time, and obtaining a good experimental result. The method discussed in this paper can provide a valuable reference for the sEMG based control for rehabilitation robot systems.

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Cited By

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Lv JYang YNiu LSun XWang LLin WRong HZou L(2023)Prediction of hand grip strength based on surface electromyographic signalsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.04.00135:5(101548)Online publication date: May-2023
https://doi.org/10.1016/j.jksuci.2023.04.001
Yang JZhao ZDu CWang WPeng QQiu JWang G(2020)The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysisExpert Review of Medical Devices10.1080/17434440.2020.1852930Online publication date: 30-Nov-2020
https://doi.org/10.1080/17434440.2020.1852930

Index Terms

KUKA Real-Time Control through Angle Estimation of Wrist from sEMG with Support Vector Regression
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI theory, concepts and models

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Published In

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

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Published: 08 December 2018

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CSAI '18

CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

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Lv JYang YNiu LSun XWang LLin WRong HZou L(2023)Prediction of hand grip strength based on surface electromyographic signalsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.04.00135:5(101548)Online publication date: May-2023
https://doi.org/10.1016/j.jksuci.2023.04.001
Yang JZhao ZDu CWang WPeng QQiu JWang G(2020)The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysisExpert Review of Medical Devices10.1080/17434440.2020.1852930Online publication date: 30-Nov-2020
https://doi.org/10.1080/17434440.2020.1852930

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sEMG-based continuous estimation of joint angles of human legs by using BP neural network

Estimation of Lower Limb Periodic Motions from sEMG Using Least Squares Support Vector Regression

A Study of sEMG-based Detection Methods of Wrist Muscle Lesions