Background: Artificial intelligence (AI) research on physical fitness posture estimation has been limited by a lack of comprehensive datasets and guidelines. This study analyzes the fitness image dataset provided by Korea's AIHub platform to advance posture estimation algorithms from exercise prescription and behavioral analysis perspectives. Objectives: To analyze fitness movements and guide correct exercise posture using AI-based visual and auditory feedback. Design: Descriptive analysis of a large-scale dataset. Methods: The study examined image and JSON labeling files from AI-Hub, analyzing 6.39 million fitness images across 41 exercise types. Data structure, exercise states, and annotation characteristics were analyzed in detail. Results: The dataset encompasses 816 distinct exercise states, captured from five camera angles with 24 key body points labeled per posture. Exercises were categorized into full-body workouts (17), barbell/dumbbell exercises (16), and furniture exercises (8). Gender distribution was 76% male and 24% female, with 41% in the 27-29 age group. The dataset allows for detailed analysis of correct and incorrect postures. Conclusion: This comprehensive analysis of the AI-Hub fitness dataset provides a robust foundation for developing AI models for fitness posture evaluation and feedback, benefiting exercise coach web/app service developers.

INTRODUCTION
    Data Specification Information, Dataset Analysis andResults
    Data statistics and distribution
    Data building overview
DISCUSSION
CONCLUSION
FUNDING
REFERENCES

• Byunggook Lee(Department of Computer Engineering, Dongseo University, Busan, Republic of Korea)
• Ulziichimeg Ulziisaikhan(Department of Computer Engineering, Dongseo University, Busan, Republic of Korea)
• Hyotaek Lim(Department of Computer Engineering, Dongseo University, Busan, Republic of Korea)
• Wansuk Choi(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea) Corresponding author