Development of a stress-free algorithm for controlling active running platforms
The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02004.pdf |
| _version_ | 1797334005334409216 |
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| author | Obukhov Artem Karpushkin Sergey Siukhin Aleksandr Patutin Kirill Averin Yaroslav |
| author_facet | Obukhov Artem Karpushkin Sergey Siukhin Aleksandr Patutin Kirill Averin Yaroslav |
| author_sort | Obukhov Artem |
| collection | DOAJ |
| description | The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems. |
| first_indexed | 2024-03-08T08:13:31Z |
| format | Article |
| id | doaj.art-755c82f112b3436cabd5234711014622 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T08:13:31Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-755c82f112b3436cabd52347110146222024-02-02T08:04:05ZengEDP SciencesITM Web of Conferences2271-20972024-01-01590200410.1051/itmconf/20245902004itmconf_hmmocs2023_02004Development of a stress-free algorithm for controlling active running platformsObukhov Artem0Karpushkin Sergey1Siukhin Aleksandr2Patutin Kirill3Averin Yaroslav4Department of Automated Systems of Decision-Making Support, Tambov State Technical UniversityDepartment of Computer Integrated Systems in Mechanical Engineering, Tambov State Technical UniversityDepartment of Computer Integrated Systems in Mechanical Engineering, Tambov State Technical UniversityDepartment of Automated Systems of Decision-Making Support, Tambov State Technical UniversityDepartment of Automated Systems of Decision-Making Support, Tambov State Technical UniversityThe problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems.https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02004.pdf |
| spellingShingle | Obukhov Artem Karpushkin Sergey Siukhin Aleksandr Patutin Kirill Averin Yaroslav Development of a stress-free algorithm for controlling active running platforms ITM Web of Conferences |
| title | Development of a stress-free algorithm for controlling active running platforms |
| title_full | Development of a stress-free algorithm for controlling active running platforms |
| title_fullStr | Development of a stress-free algorithm for controlling active running platforms |
| title_full_unstemmed | Development of a stress-free algorithm for controlling active running platforms |
| title_short | Development of a stress-free algorithm for controlling active running platforms |
| title_sort | development of a stress free algorithm for controlling active running platforms |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/02/itmconf_hmmocs2023_02004.pdf |
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