Abstract
In recent years, with the rapid development of sports, the number of people playing various sports is increasing day by day. Among them, badminton has become one of the most popular sports because of the advantages of fewer restrictions on the field and ease of learning. This paper develops a wearable sports activity classification system for accurately recognizing badminton actions. A single acceleration sensor fixed on the end of the badminton racket handle is used to collect the data of the badminton action. The sliding window segmentation technique is used to extract the hitting signal. An improved hidden Markov model (HMM) is developed to identify standard 10 badminton strokes. These include services, forehand chop, backhand chop the goal, the forehand and backhand, forehand drive, backhand push the ball, forehand to pick, pick the ball backhand, and forehand. The experimental results show that the model designed can recognize ten standard strokes in real time. Compared with the traditional HMM, the average recognition rate of the improved HMM is improved by 7.3%. The comprehensive recognition rate of the final strokes can reach up to 95%. Therefore, this model can be used to improve the competitive level of badminton players.
Highlights
Badminton is an Olympic discipline, and it is one of the most popular racket sports worldwide
E improved hidden Markov model (HMM) training and recognition processes are shown in Figure 4, the training sample data for ten kinds serve as 100 shots of all data, the corresponding model is obtained by the improved training algorithm, and for anyone to identify the strokes, the strokes of the observation are obtained by the data preprocessing algorithm sequence through the Viterbi algorithm
70% of the data was used for training and 30% for testing the HMM. e conditional probability of the occurrence of the optimal state sequence under ten batting action models was calculated, and the model corresponding to the maximum conditional probability was found. e batting action corresponding to the model was the recognition result
Summary
Badminton is an Olympic discipline, and it is one of the most popular racket sports worldwide. Sports action recognition systems are developed using machine and deep learning approaches with data measured by an inertial and magnetic sensor or by computer vision technologies [4]. An alternative approach for sports action recognition is to use inertial sensors for data collection. Several researchers have developed wearable sports activity recognition and monitoring systems, football, badminton, tennis, baseball, golf, basketball, table tennis, volleyball, etc. A wearable badminton activity recognition system is developed using the hidden Markov model (HMM) in this paper. (ii) e system uses a single acceleration sensor fixed at the end of the badminton racket handle to collect action data. It uses sliding window data segmentation technology to extract hitting signals.
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