Improving wrist angle recognition accuracy under different load conditions

Jinrong Tian, Chengcheng Li, Cuiqiao Li, Gongfa Li, Dalin Zhou, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The wrist angle estimation based on surface electromyography (sEMG) signals plays an important role in the sEMG application. This paper confirms that the accuracy of the wrist angle recognition decreases with the increase of the wrist load by the changes of the sEMG features in different loads. To address the above problem, this paper proposes a combined feature, integrating frequency-domain and time-domain features, to improve the recognition accuracy, which has been demonstrated by comparative experimental results.
Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherInstitute of Electrical and Electronics Engineers
Pages1267-1272
Number of pages6
ISBN (Electronic)978-1-7281-0770-7, 978-1-7281-0769-1
ISBN (Print)978-1-7281-0771-4
DOIs
Publication statusPublished - 16 Apr 2020
Event9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems - Suzhou, China
Duration: 29 Jul 20192 Aug 2019

Publication series

Name2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
ISSN (Print)2379-7711

Conference

Conference9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
Country/TerritoryChina
CitySuzhou
Period29/07/192/08/19

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