Accurate Recognition of Player Identity and Stroke Performance in Table Tennis Using a Smart Wristband

In table tennis, a 40 mm in diameter table tennis ball weighing 2.7g can reach the opponent's table in a very short time when it travels at a speed of 17m/s. That is difficult for new players to hit accurately. The purpose of this work is to recognize hits and misses by distinguishing the action difference between hitting and missing table tennis. The result of recognition can be used to figure out a small deviation from the correct posture and improve the hitting accuracy. Six volunteers participated in the experiment. The volunteers wore wristband sensors on the wrist on which they held the table tennis bat. Each volunteer played the ball 100 times and collected the information through a wristband sensor. The collected information was analyzed by support vector machine (SVM), decision tree (CART), linear discriminant analysis (LDA), K nearest neighbor (KNN), and Naive Bayes (NB) to identify the identities of volunteers and the hit or miss of the ball. The accuracy rate of volunteers' identity recognition is 99%, and the accuracy rate of hits and misses is 95%. The results show that the wristband sensor can accurately identify the volunteers' identities and missing cases through appropriate classification methods. This shows that we can find out the nuances between the stroke postures by hitting or missing the ball, and then use the results to correct the movement of the players, and thus improve the players' skill.