Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7199 |
| _version_ | 1797511779960487936 |
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| author | Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie |
| author_facet | Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie |
| author_sort | Jianyu Yang |
| collection | DOAJ |
| description | In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (<i>RMSE</i>), and correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>)) were calculated to verify the feasibility of the prediction method. |
| first_indexed | 2024-03-10T05:51:58Z |
| format | Article |
| id | doaj.art-6b29b93508214b259ff1d015e24db74f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:51:58Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6b29b93508214b259ff1d015e24db74f2023-11-22T21:38:09ZengMDPI AGSensors1424-82202021-10-012121719910.3390/s21217199Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee AmputeesJianyu Yang0Guanchao Li1Xiaofei Zhao2Hualong Xie3Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, ChinaIn the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (<i>RMSE</i>), and correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>)) were calculated to verify the feasibility of the prediction method.https://www.mdpi.com/1424-8220/21/21/7199asymmetric lower extremity exoskeletonelectromyographic signalsartificial neural networkjoint-angle predictiongoing up and downstairs |
| spellingShingle | Jianyu Yang Guanchao Li Xiaofei Zhao Hualong Xie Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees Sensors asymmetric lower extremity exoskeleton electromyographic signals artificial neural network joint-angle prediction going up and downstairs |
| title | Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees |
| title_full | Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees |
| title_fullStr | Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees |
| title_full_unstemmed | Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees |
| title_short | Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees |
| title_sort | research on joint angle prediction based on artificial neural network for above knee amputees |
| topic | asymmetric lower extremity exoskeleton electromyographic signals artificial neural network joint-angle prediction going up and downstairs |
| url | https://www.mdpi.com/1424-8220/21/21/7199 |
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