Improving wrist angle recognition accuracy under different load conditions

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

doi:10.1109/CYBER46603.2019.9066741

Improving wrist angle recognition accuracy under different load conditions

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

School of Computing

Wuhan University of Science and Technology

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

184 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1267-1272
Number of pages	6
ISBN (Electronic)	978-1-7281-0770-7, 978-1-7281-0769-1
ISBN (Print)	978-1-7281-0771-4
DOIs	https://doi.org/10.1109/CYBER46603.2019.9066741
Publication status	Published - 16 Apr 2020
Event	9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems - Suzhou, China Duration: 29 Jul 2019 → 2 Aug 2019

Publication series

Name	2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)
Publisher	IEEE
ISSN (Print)	2379-7711

Conference

Conference	9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
Country/Territory	China
City	Suzhou
Period	29/07/19 → 2/08/19

Access to Document

10.1109/CYBER46603.2019.9066741

Tian_et_al_AAM
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Accepted author manuscript (Post-print), 425 KB

Cite this

Tian, J., Li, C., Li, C., Li, G., Zhou, D., & Ju, Z. (2020). Improving wrist angle recognition accuracy under different load conditions. In Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 1267-1272). (2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CYBER46603.2019.9066741

Tian, Jinrong ; Li, Chengcheng ; Li, Cuiqiao et al. / Improving wrist angle recognition accuracy under different load conditions. Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1267-1272 (2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)).

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title = "Improving wrist angle recognition accuracy under different load conditions",

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.",

author = "Jinrong Tian and Chengcheng Li and Cuiqiao Li and Gongfa Li and Dalin Zhou and Zhaojie Ju",

year = "2020",

month = apr,

day = "16",

doi = "10.1109/CYBER46603.2019.9066741",

language = "English",

isbn = "978-1-7281-0771-4",

series = "2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)",

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

pages = "1267--1272",

booktitle = "Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)",

address = "United States",

note = "9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems ; Conference date: 29-07-2019 Through 02-08-2019",

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Tian, J, Li, C, Li, C, Li, G, Zhou, D & Ju, Z 2020, Improving wrist angle recognition accuracy under different load conditions. in Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Institute of Electrical and Electronics Engineers Inc., pp. 1267-1272, 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, Suzhou, China, 29/07/19. https://doi.org/10.1109/CYBER46603.2019.9066741

Improving wrist angle recognition accuracy under different load conditions. / Tian, Jinrong; Li, Chengcheng; Li, Cuiqiao et al.
Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Institute of Electrical and Electronics Engineers Inc., 2020. p. 1267-1272 (2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)).

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

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AU - Li, Chengcheng

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AU - Li, Gongfa

AU - Zhou, Dalin

AU - Ju, Zhaojie

PY - 2020/4/16

Y1 - 2020/4/16

N2 - 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.

AB - 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.

U2 - 10.1109/CYBER46603.2019.9066741

DO - 10.1109/CYBER46603.2019.9066741

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SN - 978-1-7281-0771-4

T3 - 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

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BT - Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems

Y2 - 29 July 2019 through 2 August 2019

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Tian J, Li C, Li C, Li G, Zhou D , Ju Z. Improving wrist angle recognition accuracy under different load conditions. In Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Institute of Electrical and Electronics Engineers Inc. 2020. p. 1267-1272. (2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)). doi: 10.1109/CYBER46603.2019.9066741

Improving wrist angle recognition accuracy under different load conditions

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