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<p>To improve the effectiveness of basketball running training, this paper proposes an AR/VR technology-based motion capture method for college basketball sports training. This paper first describes the method steps of virtual reality motion capture technology, data fusion and skeletal data normalization of skeletal data, and calibration to obtain the rotation matrix and displacement vector of each Kinect sensor to integrate the skeleton data. Then the data features are extracted, 3D joint position, joint velocity, joint angle and angular velocity are extracted from the fused skeleton information of each frame, and then the LSTM algorithm is used to obtain the timing information in the action sequence and to classify the action for recognition. Finally, the method’s performance is evaluated in terms of accuracy, recall, and response time. Regarding accuracy, the recognition rates of “shooting” and “defense” were around 85%, while the recognition rates of other actions were 93% and above. In terms of recognition time, the recognition time of common equipment is about 350ms, while the recognition time of virtual reality equipment is about 210ms, which is 100ms less than that of traditional equipment, demonstrating the effectiveness and feasibility of this method.</p>
</abstract>

To improve the effectiveness of basketball running training, this paper proposes an AR/VR technology-based motion capture method for college basketball sports training. This paper first describes the method steps of virtual reality motion capture technology, data fusion and skeletal data normalization of skeletal data, and calibration to obtain the rotation matrix and displacement vector of each Kinect sensor to integrate the skeleton data. Then the data features are extracted, 3D joint position, joint velocity, joint angle and angular velocity are extracted from the fused skeleton information of each frame, and then the LSTM algorithm is used to obtain the timing information in the action sequence and to classify the action for recognition. Finally, the method’s performance is evaluated in terms of accuracy, recall, and response time. Regarding accuracy, the recognition rates of “shooting” and “defense” were around 85%, while the recognition rates of other actions were 93% and above. In terms of recognition time, the recognition time of common equipment is about 350ms, while the recognition time of virtual reality equipment is about 210ms, which is 100ms less than that of traditional equipment, demonstrating the effectiveness and feasibility of this method.

Motion capture method for college basketball training based on AR/VR technology

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Motion capture method for college basketball training based on AR/VR technology"}

To improve the effectiveness of basketball running training, this paper proposes an AR/VR technology-based motion capture method for college basketball sports...

Motion capture method for college basketball training based on AR/VR technology

Online veröffentlicht: 06. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 17. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00298

Schlüsselwörter
Virtual reality technology, Basketball sports training, Motion capture, LSTM algorithm

© 2023 Chengyong Liu et al., published by Sciendo

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

Motion capture method for college basketball training based on AR/VR technology

Online veröffentlicht: 06. Sept. 2023

Seitenbereich: -

Eingereicht: 11. Nov. 2022

Akzeptiert: 17. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00298

SchlüsselwörterVirtual reality technology, Basketball sports training, Motion capture, LSTM algorithm

© 2023 Chengyong Liu et al., published by Sciendo

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

Schlüsselwörter
Virtual reality technology, Basketball sports training, Motion capture, LSTM algorithm