Design of a Real-time Multi-individual Boxing Classification System Using Multiple Mode Sensing

In this paper, a real-time multi-individual boxing classification system, which is based on FPGA, using electromyography (EMG) signals and Inertial measurement units (IMUs) is presented. The proposed system is realized on the Xilinx Ultra96 single computer board, combining EMG signals and acceleration to classify different boxing actions. An improved attitude update algorithm based on extended Kalman filter (EKF) is used to calculate the change in roll angle of the IMU during a certain punch and a correlation-based method is then applied to classify between different types of punches. The presented system achieves a latency of about 21.7ms with an accuracy of 80.0% on a laptop with AMD R7-7735H, 16GM RAM and about 74.5ms with an accuracy of 68.3% on FPGA respectively in a multi-person boxing real-time monitoring scenario. The algorithm enables real-time boxing classification with high accuracy using only wearable sensors, which enhances the portability of the whole system and can be used in real-time scenarios.