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

<p>The emerging efficacy of Virtual Reality (VR) technologies in sports training is explored in this study. We developed a VR-based motor skill training model utilizing a markerless optical motion capture system. This system employs a domain adaptive detection method in the 3D human posture realm, enabling precise identification of motor action postural feature points in students. Subsequently, the model incorporates a human dynamic motion tracking function that minimizes computational errors in motion capture data, facilitating the implementation of multi-feature fusion-based virtual digital human body motion capture technology. An empirical analysis was conducted using H College University as the case study to evaluate the effectiveness of virtual motor skill training. The findings demonstrate that the average accuracy of the proposed method for recognizing body gestures achieved a significant level of 0.834, enabling more accurate identification of students’ motor action characteristics. Moreover, the post-test results of the VR class showed marked improvements, ranging from 0.56 to 15.56 points over traditional lecture classes and 0.43 to 10.33 points over video teaching classes. These substantial variances not only underline the superiority of the VR-based training model in enhancing academic interest and physical fitness but also in advancing motor skills among learners. The study accentuates VR’s potential to augment motor skills and foresees its expansive application in future motor training domains.</p>
</abstract>

The emerging efficacy of Virtual Reality (VR) technologies in sports training is explored in this study. We developed a VR-based motor skill training model utilizing a markerless optical motion capture system. This system employs a domain adaptive detection method in the 3D human posture realm, enabling precise identification of motor action postural feature points in students. Subsequently, the model incorporates a human dynamic motion tracking function that minimizes computational errors in motion capture data, facilitating the implementation of multi-feature fusion-based virtual digital human body motion capture technology. An empirical analysis was conducted using H College University as the case study to evaluate the effectiveness of virtual motor skill training. The findings demonstrate that the average accuracy of the proposed method for recognizing body gestures achieved a significant level of 0.834, enabling more accurate identification of students’ motor action characteristics. Moreover, the post-test results of the VR class showed marked improvements, ranging from 0.56 to 15.56 points over traditional lecture classes and 0.43 to 10.33 points over video teaching classes. These substantial variances not only underline the superiority of the VR-based training model in enhancing academic interest and physical fitness but also in advancing motor skills among learners. The study accentuates VR’s potential to augment motor skills and foresees its expansive application in future motor training domains.

Research on the Application of Virtual Reality Technology in Motor Skills Training

PE Division, Sichuan University Jinjiang College

Applied Mathematics and Nonlinear Sciences

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

The emerging efficacy of Virtual Reality (VR) technologies in sports training is explored in this study. We developed a VR-based motor skill training model...

Research on the Application of Virtual Reality Technology in Motor Skills Training

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 21. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1646

Schlüsselwörter
VR, Optical motion capture, Motion tracking function, Human posture, Motor skill training

© 2024 Yinchun Tang, published by Sciendo

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

Research on the Application of Virtual Reality Technology in Motor Skills Training

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 21. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1646

SchlüsselwörterVR, Optical motion capture, Motion tracking function, Human posture, Motor skill training

© 2024 Yinchun Tang, published by Sciendo

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

Schlüsselwörter
VR, Optical motion capture, Motion tracking function, Human posture, Motor skill training