Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid

Fangqiuzi He, Yong Liu, Weiwen Zhan, Qingjie Xu and Xiaoling Chen
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Fangqiuzi He: School of Art and Design, Wuhan Polytechnic University, Wuhan 430023, China
Yong Liu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Weiwen Zhan: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Qingjie Xu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Xiaoling Chen: School of Art and Media, China University of Geosciences, Wuhan 430074, China

Energies, 2022, vol. 15, issue 6, 1-17

Abstract: 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.

Keywords: virtual reality; manual operation accuracy evaluation; graph convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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