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.

Purpose: The purpose of the study is to examine the influence of physical fitness on a nonverbal intelligence score and cortical networking. Methods: Participants were twenty four high-fit and twenty one low-fit middle school students. K-CTONI-2 was used to measure nonverbal intelligence less influenced by educational experience and verbal ability. Cortical activation at F3, F4, C3, C4, P3, P4, T3, T4, O1, and O2 has been recorded during intelligence test. Inter-hemispheric and intra-hemispheric coherences were calculated to examine cortico-cortical communication. Results: As the behavioral results, nonverbal intelligence score and accuracy were not significant different according to physical fitness. The result of coherence revealed that coherence at right hemisphere which is related to visual-spatial processing was higher in high-fit but coherence at left hemisphere related to verbal-analytic processing was higher in low-fit group. Conclusion: The results imply the greater neural efficiency during intelligence test in high-fit relative to low-fit group.