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%.
Highlights
Basketball is one of the most popular sports with a large fan base worldwide
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the IEC for Clinical Research of Shooting postures Gather step shot Hook shot Free throw Stop jump shot Pump fake Inside shot Jettison throw Lay-up Jump shot Spin jumper
A sensor fusion basketball shooting posture recognition system based on a Convolutional neural networks (CNNs) was designed
Summary
Basketball requires two teams of players to use various technical actions to compete with each other. Aacikmese et al [8], using an IMU placed on the arm, classified the six technical movements (forward-backward dribbling, left-right dribbling, regular dribbling, two-handed dribbling, shooting, and lay-up) in basketball by SVM. Zhao et al [9] used four IMUs placed on the left and right upper arms and forearms to collect basketball technical movement data, using SVM to identify dribbling, passing, catching, and shooting. These studies, which use sensors on the arms, address basic shooting postures but ignore composite shooting postures. It is not sufficient to study composite shooting postures using only arm sensor data
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