Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm
At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy...
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| Published in | Computational intelligence and neuroscience Vol. 2021; no. 1; p. 1110503 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2021/1110503 |
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| Abstract | At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results. |
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| AbstractList | At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results. At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results. |
| Audience | Academic |
| Author | Zhao, Shuwei |
| AuthorAffiliation | School of Physical Education, Xinxiang Medical University, Xinxiang, Henan 453003, China |
| AuthorAffiliation_xml | – name: School of Physical Education, Xinxiang Medical University, Xinxiang, Henan 453003, China |
| Author_xml | – sequence: 1 givenname: Shuwei orcidid: 0000-0001-5530-6849 surname: Zhao fullname: Zhao, Shuwei organization: School of Physical EducationXinxiang Medical UniversityXinxiangHenan 453003Chinaxxmu.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34745243$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1155_2023_9841827 crossref_primary_10_1155_2022_9956753 crossref_primary_10_32604_biocell_2023_027373 |
| Cites_doi | 10.1016/j.aei.2020.101136 10.1038/s43588-021-00084-1 10.1016/s0016-5085(19)39304-7 10.1109/tcyb.2018.2797176 10.1016/j.oceaneng.2018.04.039 10.1364/oe.27.031874 10.1177/0047287520921244 10.1016/j.commatsci.2019.03.037 10.1093/jamia/ocaa102 10.3390/en13071555 10.1109/tvt.2020.2981959 10.1007/s10915-021-01532-w 10.1016/j.oceaneng.2021.108714 10.1089/ees.2018.0327 10.1007/s10796-020-10022-7 10.1016/j.humov.2018.06.007 10.1109/tits.2020.3045319 10.1038/s41592-020-01048-5 10.1007/s00521-019-04682-z 10.1016/j.energy.2018.03.179 10.1038/s42256-021-00321-2 10.1016/j.geosus.2020.03.005 10.1016/j.geosus.2020.11.005 10.1093/jssam/smz056 10.1080/01431161.2018.1484961 |
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| Copyright | Copyright © 2021 Shuwei Zhao. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Shuwei Zhao. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Shuwei Zhao. 2021 |
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| References_xml | – ident: e_1_2_8_28_2 doi: 10.1016/j.aei.2020.101136 – ident: e_1_2_8_6_2 doi: 10.1038/s43588-021-00084-1 – ident: e_1_2_8_21_2 doi: 10.1016/s0016-5085(19)39304-7 – ident: e_1_2_8_26_2 doi: 10.1109/tcyb.2018.2797176 – ident: e_1_2_8_4_2 doi: 10.1016/j.oceaneng.2018.04.039 – ident: e_1_2_8_7_2 doi: 10.1364/oe.27.031874 – ident: e_1_2_8_23_2 doi: 10.1177/0047287520921244 – ident: e_1_2_8_14_2 doi: 10.1016/j.commatsci.2019.03.037 – ident: e_1_2_8_17_2 doi: 10.1093/jamia/ocaa102 – ident: e_1_2_8_27_2 doi: 10.3390/en13071555 – ident: e_1_2_8_16_2 doi: 10.1109/tvt.2020.2981959 – volume: 18 year: 2020 ident: e_1_2_8_20_2 article-title: RBF neural network-based supervisor control for maglev vehicles on an elastic track with network time-delay publication-title: IEEE Transactions on Industrial Informatics – ident: e_1_2_8_15_2 doi: 10.1007/s10915-021-01532-w – ident: e_1_2_8_13_2 doi: 10.1016/j.oceaneng.2021.108714 – ident: e_1_2_8_1_2 doi: 10.1089/ees.2018.0327 – ident: e_1_2_8_18_2 doi: 10.1007/s10796-020-10022-7 – ident: e_1_2_8_22_2 doi: 10.1016/j.humov.2018.06.007 – ident: e_1_2_8_25_2 doi: 10.1109/tits.2020.3045319 – ident: e_1_2_8_2_2 doi: 10.1038/s41592-020-01048-5 – ident: e_1_2_8_24_2 doi: 10.1007/s00521-019-04682-z – ident: e_1_2_8_5_2 doi: 10.1016/j.energy.2018.03.179 – volume: 17 start-page: 2919 year: 2020 ident: e_1_2_8_9_2 article-title: Seamless authentication: for IoT-big data technologies in smart industrial application systems publication-title: IEEE Transactions on Industrial Informatics – volume: 90 year: 2020 ident: e_1_2_8_10_2 article-title: Establishing a Genetic Algorithm-Back Propagation model to predict the pressure of girdles and to determine the model function publication-title: Textile Research Journal – ident: e_1_2_8_8_2 doi: 10.1038/s42256-021-00321-2 – ident: e_1_2_8_19_2 doi: 10.1016/j.geosus.2020.03.005 – ident: e_1_2_8_11_2 doi: 10.1016/j.geosus.2020.11.005 – ident: e_1_2_8_12_2 doi: 10.1093/jssam/smz056 – ident: e_1_2_8_3_2 doi: 10.1080/01431161.2018.1484961 – reference: 37800035 - Comput Intell Neurosci. 2023 Sep 27;2023:9841827 |
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| SubjectTerms | Accuracy Algorithms Athletes Back propagation networks Basketball Data processing Efficiency Fault tolerance Human mechanics Human motion Humans Information processing Information systems Motion Motion perception Muscles Neural networks Neural Networks, Computer Optimization Recognition Response time Sports training |
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| Title | Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm |
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