Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection

Kai Wang,Yanjiang Huang,Xianmin Zhang,Jun Ota

doi:10.3390/s18020663

Kai Wang, Yanjiang Huang + Show 2 more

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https://doi.org/10.3390/s18020663

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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.

Highlights

Various dynamic handgrip tasks are required in a number of applications, including manual fabrication and machining, handling tools, monitoring for surgery, human–robot interactions and so on [1,2,3]
The purpose of this paper is to find an appropriate wavelet scale combinations (WSC) for accurate evaluation of handgrip force from surface electromyography (SEMG) during force-varying muscle contractions of long duration
To accurately estimate the handgrip force from the SEMG signals during the long duration force-varying grip motion, this paper utilized the Monte Carlo sensitivity analysis to determine the nonlinear degree of the relationship between each wavelet scale and handgrip force from the initial nonlinear SEMG-handgrip force relationship, and used a nonlinear correlation-based scale-selection method (SCA) to obtain the suitable WSC of the scale

Summary

Introduction

Various dynamic handgrip tasks are required in a number of applications, including manual fabrication and machining, handling tools, monitoring for surgery, human–robot interactions and so on [1,2,3]. These tasks require long duration muscle contractions. To obtain real-time handgrip force measurements, one indirect measurement method is based on surface electromyography (SEMG), which is a common, non-invasive technique for analyzing muscle contractions for real-world applications [4,7,8]. SEMG signals are affected by physiological factors and non-physiological factors during prolonged muscle contractions, which affects force estimation from SEMG [1,9,10]. It is necessary to obtain valid signals from the SEMG to realize accurate prediction of handgrip force in a long duration gripping motion

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