Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid
The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous wor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/6/2071 |
| _version_ | 1827649212016230400 |
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| author | Fangqiuzi He Yong Liu Weiwen Zhan Qingjie Xu Xiaoling Chen |
| author_facet | Fangqiuzi He Yong Liu Weiwen Zhan Qingjie Xu Xiaoling Chen |
| author_sort | Fangqiuzi He |
| collection | DOAJ |
| description | The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy of test actions. First, the kernel of vectorized spatio-temporal graph convolutional of the data glove is constructed with different weights for different finger joints, and the data dimensionality reduction is also achieved. Then, different evaluation strategies are proposed for different actions. Finally, a convolution deep learning network for vectorized spatio-temporal graph is built to obtain the similarity between test actions and standard ones. The evaluation results of the proposed algorithm are compared with the subjective ones labeled by experts. The experimental results verify that the proposed action evaluation method based on the vectorized spatio-temporal graph convolutional is efficient for the manual operation accuracy evaluation in VR training systems of smart grids. |
| first_indexed | 2024-03-09T19:53:41Z |
| format | Article |
| id | doaj.art-fa0c795b37cc4baabb0e55bbc6d5610b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T19:53:41Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fa0c795b37cc4baabb0e55bbc6d5610b2023-11-24T01:04:04ZengMDPI AGEnergies1996-10732022-03-01156207110.3390/en15062071Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart GridFangqiuzi He0Yong Liu1Weiwen Zhan2Qingjie Xu3Xiaoling Chen4School of Art and Design, Wuhan Polytechnic University, Wuhan 430023, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Art and Media, China University of Geosciences, Wuhan 430074, ChinaThe standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy of test actions. First, the kernel of vectorized spatio-temporal graph convolutional of the data glove is constructed with different weights for different finger joints, and the data dimensionality reduction is also achieved. Then, different evaluation strategies are proposed for different actions. Finally, a convolution deep learning network for vectorized spatio-temporal graph is built to obtain the similarity between test actions and standard ones. The evaluation results of the proposed algorithm are compared with the subjective ones labeled by experts. The experimental results verify that the proposed action evaluation method based on the vectorized spatio-temporal graph convolutional is efficient for the manual operation accuracy evaluation in VR training systems of smart grids.https://www.mdpi.com/1996-1073/15/6/2071virtual realitymanual operation accuracy evaluationgraph convolutional neural network |
| spellingShingle | Fangqiuzi He Yong Liu Weiwen Zhan Qingjie Xu Xiaoling Chen Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid Energies virtual reality manual operation accuracy evaluation graph convolutional neural network |
| title | Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid |
| title_full | Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid |
| title_fullStr | Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid |
| title_full_unstemmed | Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid |
| title_short | Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid |
| title_sort | manual operation evaluation based on vectorized spatio temporal graph convolutional for virtual reality training in smart grid |
| topic | virtual reality manual operation accuracy evaluation graph convolutional neural network |
| url | https://www.mdpi.com/1996-1073/15/6/2071 |
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