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.

中文翻译：

---

机器学习下智能羽毛球训练机器人在运动员伤害预防中的应用

这项研究旨在探索基于机器学习算法的智能羽毛球训练机器人（IBTR）在预防羽毛球运动员受伤中的作用。从硬件和软件系统的角度设计了IBTR，并通过机器学习中的隐马尔可夫模型（HMM）识别并分析了运动员的动作。设计完成后，用计算机对其进行仿真以分析其性能。结果表明，优化HMM后，在命中时基于滑动窗口分割的数据预处理算法的应用和改进后的HMM算法的应用提高了机器人的识别率，显示了良好的识别能力。对训练集样本的影响。此外，通过不同大小的数据集分析机器人的精度后，训练数据的总大小为120组时，准确率基本稳定。因此，发现所设计的IBTR具有较高的识别率和稳定的准确性，可以为运动员训练中的预防伤害提供实验参考。