Abstract
This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent 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 analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a 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 analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.
Highlights
Today, people’s living standards have significantly improved with the rapid development of science and technology
Sliding window segmentation based on hitting time Sliding window segmentation based on the maximal value Window segmentation based on the event Motion recognition based on support vector machines (SVM) Action recognition based on traditional hidden Markov model (HMM) Action recognition based on improved HMM Same athlete Different athletes block, lifting, and chop
In terms of the recognition of training objects, the recognition rate based on the same athlete is up to 97.98%, and the average recognition rate of the system based on different athletes is up to 96.711%
Summary
People’s living standards have significantly improved with the rapid development of science and technology. As one of human beings’ greatest inventions in the era of artificial intelligence (AI), after years of development, robots already possess a considerable amount of AI. Robotics has experienced several stages maturity in its research and development. At this stage, its functions are becoming more mature, and its applicational prospects are extremely broad (Mizuno et al, 2019; Cao et al, 2020). There are some data analyses and intelligent sports products in the popular badminton sport, which initially demonstrate the appeal of applying AI technology to traditional sports (Mutaqin et al, 2020). Applying machine learning and other related algorithms to the training of badminton players has become the focus of scientific research in recent years
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