<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The Dahongquan martial arts style is characterized by its intricate and varied maneuvers, posing challenges for traditional video demonstrations to fully satisfy learners’ needs. This study introduces a cutting-edge approach utilizing a visual neural network to deconstruct and map these martial arts movements in a 3D virtual environment. Initially, preprocessing involves extracting data from exercise videos, employing a combination of video annotation and skeleton feature extraction. Subsequently, a deep residual network (DRN) with multi-scale feature extraction and dense residual attention mechanisms is employed, facilitating a systematic breakdown of the martial arts movements. The third phase involves constructing a 3D action mapping and demonstration process, utilizing cubic B-spline curve fitting and cylinder deformation techniques to enhance the representation of movement based on the skeletal model. Remarkably, the DRN network, without the need for extensive training datasets, achieves flawless recognition of various gongfu combinations and records a recognition rate of 92.92% for the most challenging boxing movements. The efficacy of this model is further corroborated by its enhancement of the demonstration process, achieving an overall learner satisfaction rating of 5.26. The disassembly and demonstration strategy proposed in this study has proven effective during experimental sessions, demonstrating significant potential for advancing martial arts training methodologies.</p>
</abstract>

The Dahongquan martial arts style is characterized by its intricate and varied maneuvers, posing challenges for traditional video demonstrations to fully satisfy learners’ needs. This study introduces a cutting-edge approach utilizing a visual neural network to deconstruct and map these martial arts movements in a 3D virtual environment. Initially, preprocessing involves extracting data from exercise videos, employing a combination of video annotation and skeleton feature extraction. Subsequently, a deep residual network (DRN) with multi-scale feature extraction and dense residual attention mechanisms is employed, facilitating a systematic breakdown of the martial arts movements. The third phase involves constructing a 3D action mapping and demonstration process, utilizing cubic B-spline curve fitting and cylinder deformation techniques to enhance the representation of movement based on the skeletal model. Remarkably, the DRN network, without the need for extensive training datasets, achieves flawless recognition of various gongfu combinations and records a recognition rate of 92.92% for the most challenging boxing movements. The efficacy of this model is further corroborated by its enhancement of the demonstration process, achieving an overall learner satisfaction rating of 5.26. The disassembly and demonstration strategy proposed in this study has proven effective during experimental sessions, demonstrating significant potential for advancing martial arts training methodologies.

Demonstration and Analysis of DRN Deep Learning Architecture for Step-by-Step Disassembly of Dahongquan

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Dahongquan martial arts style is characterized by its intricate and varied maneuvers, posing challenges for traditional video demonstrations to fully...

Demonstration and Analysis of DRN Deep Learning Architecture for Step-by-Step Disassembly of Dahongquan

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 06 mar 2024

Accettato: 27 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1636

Parole chiave
DRN deep network, Action remapping, Skeleton features, Action recognition, Dahongquan

© 2024 Xixiang Tian, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Demonstration and Analysis of DRN Deep Learning Architecture for Step-by-Step Disassembly of Dahongquan

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 06 mar 2024

Accettato: 27 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1636

Parole chiaveDRN deep network, Action remapping, Skeleton features, Action recognition, Dahongquan

© 2024 Xixiang Tian, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
DRN deep network, Action remapping, Skeleton features, Action recognition, Dahongquan