An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm

Qingjie Xu, Yong Liu and Shuo Li ()
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Qingjie Xu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Yong Liu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Shuo Li: School of International Education, Wuhan University of Technology, Wuhan 430074, China

Energies, 2024, vol. 17, issue 24, 1-13

Abstract: To address the shortcomings in action evaluation within VR simulation power training, this paper introduces a novel action recognition and evaluation method based on dynamic recognition of finger keypoints combined with an improved Dynamic Time Warping (DTW) algorithm. By constructing an action recognition model centered on hand keypoints, the proposed method integrates distance similarity and cosine similarity to account comprehensively for both numerical differences and directional consistency of action features. This approach effectively tackles the challenges of feature extraction and recognition for complex actions in VR power training. Furthermore, a scoring mechanism based on the improved DTW algorithm is proposed, which employs Gaussian-weighted feature-derivative Euclidean distance combined with cosine similarity. This method significantly reduces computational complexity while improving scoring accuracy and consistency. Experimental results indicated that the improved DTW algorithm outperformed traditional methods in terms of classification accuracy and robustness. In particular, cosine similarity demonstrated superior performance in capturing dynamic variations and assessing the consistency of fine hand movements. This study provides an essential technical reference for action evaluation in VR simulation power training and offers a scientific basis for advancing the intelligence and digitalization of power VR training environments.

Keywords: virtual reality; simulation power training; action evaluation algorithm (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: 2024
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