Detection of Tennis Strokes using Wearable Sensor

Analysis has become an important part of pro-fessional sports every major professional team employs expert analysts to improve the team. With the availability of high-quality data, high processing power, and advanced algorithms have been explored. Statistical models made an evolution in sports life. Using wearable sensor to gather data with a combination of an accelerometer and gyroscope of 3 principles axes. That sensor will be worn on wrist of player. Each stroke performed would be saved with its type in the sensor hardware then sent to PC wirelessly for more plays analysis. Using machine learning technique (ANN) on data to analyze for getting detection stroke type and prediction how accurate the player would play in the further plays according to player statistics. The accuracy recorded for this project is 96 percent on classification. For upgrading the strategy-making information predisposing procedures to evaluate data quality. The assessment of the concocted strategy shows promising outcomes contrasted with a comparable technique.