Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

Abstract

In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.