Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning - Xie, Jun; Chen, Guohua; Liu, Shuang (Frontiers in Neurorobotics, 2021)
This study is developed to explore the role of intelligent badminton training robot (IBTR) in the prevention of badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analysed with the hidden Markov model (HMM) in the machine learning. After the design is completed, it is simulated with the computer to analyse its performance. The results show that after the HMM is optimized, application of the data pre-processing algorithm based on the sliding window segmentation at the moment of hitting and the application of the modified HMM algorithm have improved the recognition rate of the robot, and displayed good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets after the accuracy of the robot is analysed through different data set sizes. Therefore, it is found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.