Entropy of surface EMG reflects object weight in grasp-and-lift task

Yuqi Li; Beth Jelfs; Rosa H.M. Chan

doi:10.1109/EMBC.2017.8037372

Entropy of surface EMG reflects object weight in grasp-and-lift task

Yuqi Li, Beth Jelfs, Rosa H.M. Chan

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

1 Citation (Scopus)

Abstract

Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2530-2533
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037372
Publication status	Published - 13 Sept 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17

Bibliographical note

Funding Information:
*This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU110813].

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2017.8037372

Cite this

Li, Y., Jelfs, B., & Chan, R. H. M. (2017). Entropy of surface EMG reflects object weight in grasp-and-lift task. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 2530-2533). Article 8037372 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2017.8037372

Li, Yuqi ; Jelfs, Beth ; Chan, Rosa H.M. / Entropy of surface EMG reflects object weight in grasp-and-lift task. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 2530-2533 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Entropy of surface EMG reflects object weight in grasp-and-lift task",

abstract = "Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.",

author = "Yuqi Li and Beth Jelfs and Chan, {Rosa H.M.}",

note = "Funding Information: *This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU110813]. Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

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doi = "10.1109/EMBC.2017.8037372",

language = "English",

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Li, Y, Jelfs, B & Chan, RHM 2017, Entropy of surface EMG reflects object weight in grasp-and-lift task. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037372, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers (IEEE), pp. 2530-2533, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 11/07/17. https://doi.org/10.1109/EMBC.2017.8037372

Entropy of surface EMG reflects object weight in grasp-and-lift task. / Li, Yuqi; Jelfs, Beth; Chan, Rosa H.M.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 2530-2533 8037372 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

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PY - 2017/9/13

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N2 - Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.

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Li Y, Jelfs B, Chan RHM. Entropy of surface EMG reflects object weight in grasp-and-lift task. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 2530-2533. 8037372. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037372

Entropy of surface EMG reflects object weight in grasp-and-lift task

Abstract

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Bibliographical note

ASJC Scopus subject areas

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