Adaptive Badminton Stroke Classification by ANN Backward Propagation

• Main Authors: Wang, Chih-Hao, 王致皓
• Other Authors: Yi, Chih-Wei
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/ee5r4t

碩士 === 國立交通大學 === 網路工程研究所 === 106 === We have developed a smart badminton racket prototype with machine learning labeled technique to do automatic stroke type classification by Random Forest, SMO and Naïve Bayes. In the previous works, it shown that there is a gap between general model and personal model with accuracy 80% to 95% in average. However, the previous machine learning technique such as Random Forest, SMO and Naïve Bayes is batch processing and don’t have fine-tune method from general to personal model. In this work, we would like to design based on Neural Network and applied back-propagation to have the chance to modify general to personal. On the other hand, we develop an App to realize our ideas. It is based on wearable devices and mobile computing to log stroke types in real time. Through the video program can label stroke types in flexible and automatic. Let two mobile phone programs communicate with each other to battle a game and score points. By combining with cloud service, it could be used to analyze personal style of play and game record in a long period. To improve the badminton rally ability of players by finding out the weakness of them.