TennisEye: Tennis Ball Speed Estimation using a Racket-mounted Motion Sensor

Aggressive tennis shots with high ball speed are the key factor in winning a tennis match. Today's tennis players are increasingly focused on improving ball speed. As a result, in recent tennis tournaments, records of tennis shot speeds are broken again and again. The traditional method for cal...

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Veröffentlicht in:Proceedings of the 18th International Conference on Information Processing in Sensor Networks S. 241 - 252
Hauptverfasser: Zhao, Hongyang, Wang, Shuangquan, Zhou, Gang, Jung, Woosub
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.04.2019
Schlagworte:
Acceleration
Estimation
Motion detection
motion sensor
physical model
regression model
Sensor systems
Sports
Sports equipment
tennis ball speed estimation
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Zusammenfassung:Aggressive tennis shots with high ball speed are the key factor in winning a tennis match. Today's tennis players are increasingly focused on improving ball speed. As a result, in recent tennis tournaments, records of tennis shot speeds are broken again and again. The traditional method for calculating the tennis ball speed uses multiple high-speed cameras and computer vision technology. This method is very expensive and hard to set up. Another way to calculate the tennis ball speed is to use motion sensors, which are lower cost and easier to set up. In this paper, we propose an approach for tennis ball speed estimation based on a racket-mounted motion sensor. We divide the tennis strokes into three categories: serve, groundstroke, and volley. For a serve, a regression model is proposed to estimate the ball speed. For a groundstroke or volley, two models are proposed: a regression model and a physical model. We use the physical model to estimate the ball speed for advanced players and the regression model for beginner players. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work. CCS CONCEPTS * Human-centered computing →Ubiquitous computing; Mobile computing; Mobile devices;
DOI:10.1145/3302506.3310404