Application of statistical techniques and artificial neural network to estimate force from sEMG signals
This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. Ther...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2016-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_593_6db5e7ffbd72ce4aae887b4881c00a09.pdf |
| _version_ | 1818944113230217216 |
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| author | V. Khoshdel A. R Akbarzadeh |
| author_facet | V. Khoshdel A. R Akbarzadeh |
| author_sort | V. Khoshdel |
| collection | DOAJ |
| description | This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals. |
| first_indexed | 2024-12-20T07:38:03Z |
| format | Article |
| id | doaj.art-58ada12c75774ea8896babb43fc222a7 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-20T07:38:03Z |
| publishDate | 2016-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-58ada12c75774ea8896babb43fc222a72022-12-21T19:48:13ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442016-07-014213514110.5829/idosi.JAIDM.2016.04.02.02593Application of statistical techniques and artificial neural network to estimate force from sEMG signalsV. Khoshdel0A. R Akbarzadeh1Center of Excellence on Soft Computing & Intelligent Information Processing, Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad.Center of Excellence on Soft Computing & Intelligent Information Processing, Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad.This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals.http://jad.shahroodut.ac.ir/article_593_6db5e7ffbd72ce4aae887b4881c00a09.pdfArtificial Neural NetworkTaguchi methodAnalysis of varianceEMG signals |
| spellingShingle | V. Khoshdel A. R Akbarzadeh Application of statistical techniques and artificial neural network to estimate force from sEMG signals Journal of Artificial Intelligence and Data Mining Artificial Neural Network Taguchi method Analysis of variance EMG signals |
| title | Application of statistical techniques and artificial neural network to estimate force from sEMG signals |
| title_full | Application of statistical techniques and artificial neural network to estimate force from sEMG signals |
| title_fullStr | Application of statistical techniques and artificial neural network to estimate force from sEMG signals |
| title_full_unstemmed | Application of statistical techniques and artificial neural network to estimate force from sEMG signals |
| title_short | Application of statistical techniques and artificial neural network to estimate force from sEMG signals |
| title_sort | application of statistical techniques and artificial neural network to estimate force from semg signals |
| topic | Artificial Neural Network Taguchi method Analysis of variance EMG signals |
| url | http://jad.shahroodut.ac.ir/article_593_6db5e7ffbd72ce4aae887b4881c00a09.pdf |
| work_keys_str_mv | AT vkhoshdel applicationofstatisticaltechniquesandartificialneuralnetworktoestimateforcefromsemgsignals AT arakbarzadeh applicationofstatisticaltechniquesandartificialneuralnetworktoestimateforcefromsemgsignals |