Abstract
The sensors can collect the athletes' movement data in real time, providing more accurate and objective feedback for the training process. This study aims to explore the application of video behavior rapid detection based on sports wearable sensor devices in sports training. By integrating sensor data and video information, more comprehensive and accurate behavioral analysis is provided to help coaches better understand athlete performance and progress. The study uses an approach based on motion wearable sensor devices and video technology, in which athletes wear sensor devices and exercise during training. Sensors automatically record movement data, and cameras record video of the training process. Then, computer vision algorithms and machine learning techniques are used, combined with sensor data and video information, for rapid behavior detection. The experimental results show that the video behavior detection based on sports wearable sensor devices has high accuracy and efficiency in sports training. Through the fusion of sensor data and video information, the movement, posture and technical details of the athlete can be analyzed in real time, and accurate evaluation results can be obtained.
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