Abstract
Currently, artificial intelligence and virtual technology are widely used in motion monitoring. Virtual entertainment robots can provide adaptive and personalized fitness services for different user groups, allowing users to experience the exercise process in games. This article studies the virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition. Firstly, this article explores deep learning methods for motion recognition. By constructing a generative adversarial network to obtain discrimination results, and calculating its anti-loss function. This approach can improve the efficiency of human body movement style conversion through adversarial training. Finally, through the evaluation of movement posture and action recognition, quantitative evaluation results and targeted guidance strategies can be provided to help correct incorrect movements and master correct techniques. Using motion capture systems to identify user movements during exercise not only accurately extracts user fitness movement information, but also makes it easier to use and reduces user costs. The system uses motion evaluation techniques for evaluation to help users correct incorrect fitness habits, prevent exercise losses, and exercise more safely and effectively.
Published Version
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