An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm
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 reco...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/24/6242 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850041832325513216 |
|---|---|
| author | Qingjie Xu Yong Liu Shuo Li |
| author_facet | Qingjie Xu Yong Liu Shuo Li |
| author_sort | Qingjie Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ada5ad53ea2d4b0a9ca77cfd46e71ca5 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-ada5ad53ea2d4b0a9ca77cfd46e71ca52025-08-20T02:55:41ZengMDPI AGEnergies1996-10732024-12-011724624210.3390/en17246242An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping AlgorithmQingjie Xu0Yong Liu1Shuo Li2School 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 International Education, Wuhan University of Technology, Wuhan 430074, ChinaTo 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.https://www.mdpi.com/1996-1073/17/24/6242virtual realitysimulation power trainingaction evaluation algorithm |
| spellingShingle | Qingjie Xu Yong Liu Shuo Li An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm Energies virtual reality simulation power training action evaluation algorithm |
| title | An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm |
| title_full | An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm |
| title_fullStr | An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm |
| title_full_unstemmed | An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm |
| title_short | An Action Evaluation Method for Virtual Reality Simulation Power Training Based on an Improved Dynamic Time Warping Algorithm |
| title_sort | action evaluation method for virtual reality simulation power training based on an improved dynamic time warping algorithm |
| topic | virtual reality simulation power training action evaluation algorithm |
| url | https://www.mdpi.com/1996-1073/17/24/6242 |
| work_keys_str_mv | AT qingjiexu anactionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm AT yongliu anactionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm AT shuoli anactionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm AT qingjiexu actionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm AT yongliu actionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm AT shuoli actionevaluationmethodforvirtualrealitysimulationpowertrainingbasedonanimproveddynamictimewarpingalgorithm |