EMG pattern classification using SOFMs for hand signal recognition

Eom, Ki-Hwan; Choi, Yong-Sik; Sirisena, H.

doi:10.1007/s00500-001-0158-2

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…To substitute u n with the new membership sample u þ n , first the values u n and u þ n must be obtained by (14), then u þ n replaces u n with the condition (15) [28].…”

Section: Bayesian Fuzzy Clustering-based Fusionmentioning

confidence: 99%

“…The computational cost of TD features is less than the computational of FD features, yet yield comparable classification accuracy [10].. The classification accuracy in the classification model depends on the type of classification algorithm (i.e., supervised or unsupervised) and the features which are selected [12], [14,15]. Also, it depends on the type of subject (able-bodied or amputee) [3].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the analyses approaches designed based on muscle synergy patterns can be preferable to the analyses approaches based on the electromyography of the individual muscle distinctly. But most of the related published works have been designed based on electromyography of the individual muscle distinctly [13][14][15][16][17]. Hence, researchers believe that controlling and detecting neural information based on synergistic patterns [3,8,[18][19][20][21] is preferable than other methods [2,[4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A muscle synergies-based movements detection approach for recognition of the wrist movements

Masoumdoost

Saadatyar

Kobravi

2020

EURASIP J. Adv. Signal Process.

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Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.

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“…To substitute u n with the new membership sample u þ n , first the values u n and u þ n must be obtained by (14), then u þ n replaces u n with the condition (15) [28].…”

Section: Bayesian Fuzzy Clustering-based Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A muscle synergies-based movements detection approach for recognition of the wrist movements

Masoumdoost

Saadatyar

Kobravi

2020

EURASIP J. Adv. Signal Process.

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show abstract

“…So far, many researches tended to classify several EMG patterns of single or joint muscle contractions rather than the exerting force of the hand. There methods generally combined feature extraction (AR model [7] and pattern recognition techniques(HMM [8], SMO [9], SVM [10]), can efficiently discriminate particular finger motions or specified hand gestures. Because of merely compressing the resourceful EMG signal into a few muscular contraction modes, the signal into a relatively high level.…”

Section: Figure I Prosthetic Hand Control Diagram Using Emgmentioning

confidence: 99%

Force Estimation for Prosthetic Hands Using Surface Electromyography

Deng

Duan²,

Deng³

2015

Proceedings of the 2015 International Conference on Test, Measurement and Computational Methods

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In order to improve amputees quality of life, electromyogram (EMG) prosthetic hands have been developed to compensate for some daily activities. A force estimation method for prosthetic hands using surface electromyotraphy is proposed in the paper to help amputees. One hand of the subject is tied to imitate amputees. The subjects would grip the force sensor with his healthy hand, while doing the same thing with the tied hand. Meanwhile, EMG signals and force are acquired by LABVIEW. Then a muscle-force model based on stepwise regression method is established .The experiments are carried out to evaluate the grasping force on a single degree of freedom prosthetic hand. The experimental results show that the proposed method is effective.

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“…But, so far in literature, so many researches tended to classify several EMG patterns of single or joint muscle contractions rather than the exerting force of the hand. These methods generally combined feature extraction (AR model [1], wavelet transform [2]) with pattern recognition techniques (HMM [3], SMO [4], SVM [5,6]), can efficiently discriminate particular finger motions or specified hand gestures. Then, these classified EMG modes was mapped into prosthetic hand's motions through a simple way: relevant finger(s) moved to a pre-defined position with a fixed or optimized velocity and force.…”

Section: Introductionmentioning

confidence: 99%

Estimation of hand grasp force based on forearm surface EMG

Yang

Zhao

et al. 2009

2009 International Conference on Mechatronics and Automation

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In the force control of multi-functional prosthetic hands, it is important to extract grasp force information besides mode specifications directly from the myoelectric signals. In this paper, a force sensor is adopted to record the hand's enveloping force when the hand is performing several grasp modes, synchronously with 6 channels surface electromyography (EMG) which are extracting from the subject's forearm. Three pattern regression methods, locally weighted projection regression (LWPR), artificial neural network (ANN) and support vector machine (SVM) are used to find the best representative relationship of these two kinds of signals. Experimental results show that the SVM method is better than LWPR and ANN, especially in the case of cross-session validation. Also, the force regression performance is better when grasping within several specific modes than grasping randomly. Based on these results, an efficient online prediction of the hand grasp force is present finally, with an accuracy of around 0.9 in squared correlation coefficient (SCC) and 5--10N error over a range of 60N. It can be utilized for the prosthetic hand's control to provide a reasonable exerting force reference.

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EMG pattern classification using SOFMs for hand signal recognition

Cited by 8 publications

References 7 publications

A muscle synergies-based movements detection approach for recognition of the wrist movements

A muscle synergies-based movements detection approach for recognition of the wrist movements

Force Estimation for Prosthetic Hands Using Surface Electromyography

Estimation of hand grasp force based on forearm surface EMG

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