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This study aims to enhance sports behavior assessment using a deep learning framework for motion recognition and evaluation.

Intelligent Assessment of Healthy Behavior in Digital Exercise Development Based on Pose Estimation and Deep Learning

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This study aims to enhance sports behavior assessment using a deep learning framework for motion recognition and evaluation.
Developed a prototype system for integrating data acquisition, behavior recognition, and evaluation.
Utilized a pose estimation model for extracting skeletal keypoints from videos.
Conducted comparative experiments to evaluate system performance and user experience.
Achieved a Top-1 accuracy of 94.117% for motion recognition under image-based conditions.
The key action point recognition rate reached 98.62%, with an average inference time of 12.487 ms.
Overall system acceptance was rated at 95.38%, with feedback actionability rated at 4.88 out of 5.

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Randomized trial demonstrates improved sports behavior evaluation in digital sports, suggesting enhanced user experience and feedback quality.

Liang et al. (2026) studied this question.

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Randomized trial demonstrates improved sports behavior evaluation in digital sports, suggesting enhanced user experience and feedback quality.