Online Grasp Force Estimation From the Transient EMG

Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects’ forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized.

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Grasp force estimation from the transient EMG using high-density surface recordings

Journal of Neural Engineering ◽

10.1088/1741-2552/ab673f ◽

2020 ◽

Vol 17 (1) ◽

pp. 016052 ◽

Cited By ~ 2

Author(s):

Itzel Jared Rodriguez Martinez ◽

Andrea Mannini ◽

Francesco Clemente ◽

Angelo Maria Sabatini ◽

Christian Cipriani

Keyword(s):

High Density ◽

Force Estimation ◽

Density Surface ◽

Grasp Force

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Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation

Applied Bionics and Biomechanics ◽

10.1155/2021/8817480 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Jinfeng Wang ◽

Muye Pang ◽

Peixuan Yu ◽

Biwei Tang ◽

Kui Xiang ◽

...

Keyword(s):

Muscle Fatigue ◽

Surface Electromyography ◽

Training Data ◽

Coefficient Of Determination ◽

Force Estimation ◽

Estimation Model ◽

Substantial Impact ◽

Additional Input ◽

Hand Grasp ◽

Grasp Force

Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value.

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