A Badminton Stroke Type Recognition Model based on Machine Learning

The article Aims to compare the application effect of different machine learning algorithms in the recognition of badminton stroke types, so as to better meet the needs of quantitative analysis of badminton skills and tactics, and provide a more reasonable basis for the layout of game strategies. We used wearable inertial sensor device on the wrist to collect a total of 708 shots of linear acceleration and angular velocity data, and extract the domain features including data range, minimum maximum, average, absolute average, kurtosis, skewness statistics and P value, entropy, standard deviation, Angle range, quartile spacing, maximum minimum relative position. Then, we labeled each hit type data manually, total divided into the forehand overarm, backhand overarm, forehand underarm, backhand underarm, forehand smash. The results show that: (1) K-nearest neighbors and k-means clustering show poor prediction accuracy and application accuracy; (2) Logistic regression shows great accuracy in recognizing the overarm stroke, while random forest performs relatively well in recognizing underarm stroke; (3) The angular velocity data of the gyroscope is the characteristic that affects the classification recognition effect, and the difference in stroke type shown in wrist is mainly the angular velocity change.