<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>For wearable device motion monitoring, the accuracy rate is not high, and the background is complex. The motion tracking problem can be studied deeply using deep learning methods. In this paper, we design an instance-aware feature extraction network SiamRPN++, which can effectively distinguish different instances of the same object and avoid the “drift” of the tracker due to similar interfering objects. Secondly, a template update strategy based on mapped gradient (PGD) is proposed to address the problem of dramatic changes in target morphology during the motion tracking process. Finally, the PGD-based algorithm updates the templates effectively during the tracking process to avoid the failure of the tracking task due to the change in target morphology. The experimental results demonstrate that the target tracking algorithm using SiamRPN++ improves by a 12.24% success rate and 14.41% accuracy when tested on the target tracking dataset. The target tracking algorithm using the PGD template update strategy improves by 9.67% in success rate and 14.61% in accuracy. Wearable devices based on deep learning algorithms can achieve scientific guidance of sports exercise to provide strong support for avoiding the risk of high-intensity exercise and escorting safe and healthy sports.</p>
</abstract>

For wearable device motion monitoring, the accuracy rate is not high, and the background is complex. The motion tracking problem can be studied deeply using deep learning methods. In this paper, we design an instance-aware feature extraction network SiamRPN++, which can effectively distinguish different instances of the same object and avoid the “drift” of the tracker due to similar interfering objects. Secondly, a template update strategy based on mapped gradient (PGD) is proposed to address the problem of dramatic changes in target morphology during the motion tracking process. Finally, the PGD-based algorithm updates the templates effectively during the tracking process to avoid the failure of the tracking task due to the change in target morphology. The experimental results demonstrate that the target tracking algorithm using SiamRPN++ improves by a 12.24% success rate and 14.41% accuracy when tested on the target tracking dataset. The target tracking algorithm using the PGD template update strategy improves by 9.67% in success rate and 14.61% in accuracy. Wearable devices based on deep learning algorithms can achieve scientific guidance of sports exercise to provide strong support for avoiding the risk of high-intensity exercise and escorting safe and healthy sports.

Deep learning algorithm-based wearable device in basketball motion dynamic analysis

Department of Tourism Art, Shanghai Pudong Foreign Affairs Services School

Applied Mathematics and Nonlinear Sciences

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

For wearable device motion monitoring, the accuracy rate is not high, and the background is complex. The motion tracking problem can be studied deeply using...

Deep learning algorithm-based wearable device in basketball motion dynamic analysis

Online veröffentlicht: 01. Aug. 2023

Seitenbereich: -

Eingereicht: 08. Okt. 2022

Akzeptiert: 09. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00120

Schlüsselwörter
Wearable devices, Deep learning, Motion tracking, Feature extraction network, Template update

© 2023 Feng Chen et al., published by Sciendo

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

Deep learning algorithm-based wearable device in basketball motion dynamic analysis

Online veröffentlicht: 01. Aug. 2023

Seitenbereich: -

Eingereicht: 08. Okt. 2022

Akzeptiert: 09. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00120

SchlüsselwörterWearable devices, Deep learning, Motion tracking, Feature extraction network, Template update

© 2023 Feng Chen et al., published by Sciendo

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

Schlüsselwörter
Wearable devices, Deep learning, Motion tracking, Feature extraction network, Template update