Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization.
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| Title: | Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization. |
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| Authors: | Soomro, Mumtaz Hussain1, Conforto, Silvia1, Giunta, Gaetano1, Ranaldi, Simone1, De Marchis, Cristiano1 |
| Source: | Applied Bionics & Biomechanics. 5/8/2018, p1-10. 10p. |
| Subjects: | Muscle contraction, Electromyography, Factorization, Motor ability, Biomedical engineering |
| Abstract: | The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Bionics & Biomechanics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 129512496 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Soomro%2C+Mumtaz+Hussain%22">Soomro, Mumtaz Hussain</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Conforto%2C+Silvia%22">Conforto, Silvia</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Giunta%2C+Gaetano%22">Giunta, Gaetano</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ranaldi%2C+Simone%22">Ranaldi, Simone</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22De+Marchis%2C+Cristiano%22">De Marchis, Cristiano</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Bionics+%26+Biomechanics%22">Applied Bionics & Biomechanics</searchLink>. 5/8/2018, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Muscle+contraction%22">Muscle contraction</searchLink><br /><searchLink fieldCode="DE" term="%22Electromyography%22">Electromyography</searchLink><br /><searchLink fieldCode="DE" term="%22Factorization%22">Factorization</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+ability%22">Motor ability</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+engineering%22">Biomedical engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Bionics & Biomechanics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/2018/3629347 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Muscle contraction Type: general – SubjectFull: Electromyography Type: general – SubjectFull: Factorization Type: general – SubjectFull: Motor ability Type: general – SubjectFull: Biomedical engineering Type: general Titles: – TitleFull: Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Soomro, Mumtaz Hussain – PersonEntity: Name: NameFull: Conforto, Silvia – PersonEntity: Name: NameFull: Giunta, Gaetano – PersonEntity: Name: NameFull: Ranaldi, Simone – PersonEntity: Name: NameFull: De Marchis, Cristiano IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 05 Text: 5/8/2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 11762322 Titles: – TitleFull: Applied Bionics & Biomechanics Type: main |
| ResultId | 1 |
