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.
Highlights
With the rapid development of microelectronics technology, electronic devices are becoming more and more intelligent and humanized
Based on the flexible manipulator and its kinematics model, an experimental platform for position and attitude monitoring and control of flexible manipulator is built. e kinematic model designed by this method has certain effectiveness and feasibility
The above methods still have the problems of low monitoring accuracy, long time, and poor effect
Summary
With the rapid development of microelectronics technology, electronic devices are becoming more and more intelligent and humanized. Reference [5] proposed an interframe motion detection method for large-scale dynamic image sequences. Is method can effectively reduce the interference vector in dynamic image sequence and greatly improve the accuracy and robustness of interframe motion detection. Aiming at the above problems, a basketball training posture monitoring method based on intelligent wearable devices is proposed. E time series of the collected acceleration and angular velocity signals are segmented, and the basketball training posture characteristics are extracted from the sensor signals of the intelligent wearable device. E basketball training posture monitoring effect of this method is better, the monitoring accuracy is higher, and the monitoring time is shorter. E rest of the article is organized as follows: Section 2 focuses on the intelligent wearable device, while Section 3 throws light on the basketball training posture monitoring method.
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