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Sensors 2018, 18(2), 663; https://doi.org/10.3390/s18020663

Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection

1
Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou 510640, China
2
Research into Artifacts, Center for Engineering, University of Tokyo, Chiba 113-8654, Japan
*
Author to whom correspondence should be addressed.
Received: 15 January 2018 / Revised: 17 February 2018 / Accepted: 22 February 2018 / Published: 24 February 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Abstract

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods. View Full-Text
Keywords: surface electromyography; handgrip force; force-varying muscle contraction; nonlinear analysis; wavelet scale selection surface electromyography; handgrip force; force-varying muscle contraction; nonlinear analysis; wavelet scale selection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, K.; Zhang, X.; Ota, J.; Huang, Y. Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection. Sensors 2018, 18, 663.

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