Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Soccer shot training system based on CNN

Authors
Zheming Yang, Qi Cheng, Qiuhong Jia
Corresponding Author
Zheming Yang
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.145How to use a DOI?
Keywords
Deep learning, CNN, Embedded, Football shot, Train.
Abstract
The system uses STM32F103 as the main control chip and measures the instantaneous acceleration and angle of the shot by controlling the MPU-6050.Our motion data by hardware to collect a large number of professional players of the instep shot. Based on this data set to train the CNN model we build. We transplanted the trained model to the mobile phone. When the user shoots the door, the device transfers the data to the mobile APP through the HC-05 Bluetooth module. Then the level of shooting is judged by a good model of training so that the pertinent suggestions are made. This system implements the visualization of training data. For a beginner in football, it can help the children to practice effectively. It can also help the coaches to effectively monitor the training of the players and guide the guidance. And it is easy to operate and is suitable for popularization.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
DOI
https://doi.org/10.2991/mecae-18.2018.145How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zheming Yang
AU  - Qi Cheng
AU  - Qiuhong Jia
PY  - 2018/03
DA  - 2018/03
TI  - Soccer shot training system based on CNN
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/mecae-18.2018.145
DO  - https://doi.org/10.2991/mecae-18.2018.145
ID  - Yang2018/03
ER  -