Basketball Training Posture Monitoring Based on Intelligent Wearable Device

Abstract

Aiming at the problems of low monitoring accuracy, long time, and poor effect in the current basketball training posture monitoring method, a basketball training posture monitoring method based on intelligent wearable devices is proposed. By analyzing the concept and classification of intelligent wearable devices, the attitude monitoring technology based on intelligent wearable devices is studied. A two-stage Kalman filter is used to correct the error caused by the drift of the gyroscope signal in the intelligent wearable device by constructing an adaptive acceleration error covariance matrix. The time sequence of collecting acceleration and angular velocity signals is segmented, and the characteristics of basketball training posture are extracted from the sensor signals of the intelligent wearable device. The SVM classification algorithm is used to monitor the basketball training posture and realize the basketball training posture monitoring. The experimental results show that the basketball training posture monitoring effect of the proposed method is better, which can effectively improve the monitoring accuracy and shorten the monitoring time.