Continuous Estimation of Upper Limb Joint Angle Based on Stacked Denoising Autoencoder
In the human-robot interaction system of the rehabilitation robot for stroke rehabilitation, surface electromyography (sEMG) signal-based continuous joint angle estimation has essential significance and implementation value. However, the existing intra-subject mode is time-consuming and lacks genera...
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| Vydané v: | Journal of physics. Conference series Ročník 2402; číslo 1; s. 12043 - 12050 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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Bristol
IOP Publishing
01.12.2022
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | In the human-robot interaction system of the rehabilitation robot for stroke rehabilitation, surface electromyography (sEMG) signal-based continuous joint angle estimation has essential significance and implementation value. However, the existing intra-subject mode is time-consuming and lacks generality, while the adoption of the new inter-subject mode tests the model’s generalization ability; at the same time, the often-adopted multi-feature fusion strategy makes the feature dimensionality too high and increases the computational pressure of the system. In this regard, firstly, four time-domain features of multi-channel sEMG are extracted as the initial features; then, a stacked denoising autoencoder (SDAE) network is constructed to encode the initial set of sEMG features in low dimensions and extract more robust low-dimensional features; finally, an LSTM network is introduced as the regression network between sEMG features and joint angles. The results indicate that the feature extraction method proposed is superior to other methods and can be used for the control of the rehabilitation robot with a stable and accurate continuous joint angle estimation during motion. |
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| AbstractList | In the human-robot interaction system of the rehabilitation robot for stroke rehabilitation, surface electromyography (sEMG) signal-based continuous joint angle estimation has essential significance and implementation value. However, the existing intra-subject mode is time-consuming and lacks generality, while the adoption of the new inter-subject mode tests the model’s generalization ability; at the same time, the often-adopted multi-feature fusion strategy makes the feature dimensionality too high and increases the computational pressure of the system. In this regard, firstly, four time-domain features of multi-channel sEMG are extracted as the initial features; then, a stacked denoising autoencoder (SDAE) network is constructed to encode the initial set of sEMG features in low dimensions and extract more robust low-dimensional features; finally, an LSTM network is introduced as the regression network between sEMG features and joint angles. The results indicate that the feature extraction method proposed is superior to other methods and can be used for the control of the rehabilitation robot with a stable and accurate continuous joint angle estimation during motion. |
| Author | Li, Donglin Zhang, Yan Wang, Jianhui Pei, Xinglong Wen, Liqun |
| Author_xml | – sequence: 1 givenname: Liqun surname: Wen fullname: Wen, Liqun organization: College of Information Science and Engineering, Northeastern University , China – sequence: 2 givenname: Donglin surname: Li fullname: Li, Donglin organization: College of Information Science and Engineering, Northeastern University , China – sequence: 3 givenname: Xinglong surname: Pei fullname: Pei, Xinglong organization: College of Information Science and Engineering, Northeastern University , China – sequence: 4 givenname: Yan surname: Zhang fullname: Zhang, Yan organization: School of Electronic Information Engineering, Ningxia Institute of Science and Technology , China – sequence: 5 givenname: Jianhui surname: Wang fullname: Wang, Jianhui organization: College of Information Science and Engineering, Northeastern University , China |
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| Cites_doi | 10.1109/LRA.2021.3097272 10.1016/j.bspc.2020.102024 |
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| DOI | 10.1088/1742-6596/2402/1/012043 |
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| References | Wang (JPCS_2402_1_012043bib2) 2020; 2020 Ma (JPCS_2402_1_012043bib1) 2020; 61 Ma (JPCS_2402_1_012043bib3) 2021; 6 Deng (JPCS_2402_1_012043bib5) 2020; 12 Chen (JPCS_2402_1_012043bib4) 2019; 7 |
| References_xml | – volume: 2020 year: 2020 ident: JPCS_2402_1_012043bib2 article-title: Estimation of continuous joint angles of upper limb based on sEMG by using GA-Elman neural network publication-title: Math. Probl. Eng. – volume: 12 year: 2020 ident: JPCS_2402_1_012043bib5 article-title: Angle estimation for knee joint movement based on PCA-RELM algorithm publication-title: Symmetry-Basel – volume: 6 start-page: 7217 year: 2021 ident: JPCS_2402_1_012043bib3 article-title: A bi-directional LSTM network for estimating continuous upper limb movement from surface electromyography publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2021.3097272 – volume: 61 year: 2020 ident: JPCS_2402_1_012043bib1 article-title: Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2020.102024 – volume: 7 year: 2019 ident: JPCS_2402_1_012043bib4 article-title: A continuous estimation model of upper limb joint angles by using surface electromyography and deep learning method publication-title: IEEE Access |
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| SubjectTerms | Electromyography Feature extraction Human engineering Noise reduction Physics Rehabilitation Rehabilitation robots Robot control Robots Robustness (mathematics) |
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| Title | Continuous Estimation of Upper Limb Joint Angle Based on Stacked Denoising Autoencoder |
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