Design and implement a mobile badminton stroke classification system

The use of the badminton stroke strategy in the evenly matched game is often the key to victory. In this work, a smart racket based on wearable sensors is proposed to collect the data of swing of badminton. A cell phone APP with machine learning techniques is implemented to record stroke types automatically. In each stroke hit event, this prototype system uses Bluetooth earphone to collect the sound for detecting the accuracy time. It uses the data of IMU in each stroke for determining stroke type. Compared to EMU only solution, the system will reduce the false count of stroke hit. Using cloud techniques could record the training and game record in a long period. Overall the accuracy of stroke hit event is almost 100% by using voice print. The data of EMU is classified by Random Forest or SMO. The accuracy for personal model is 95.91%, and it is 7932% for general model. We develop a stroke record system which is combined with Wearable sensor, Mobile platform and Cloud service.