Background: The increasing demand for real-time professional fitness coaching has led to a need for accurate exercise posture recognition using artificial intelligence. Objectives: To compare the performance of Feedforward Neural Network (FNN) and Stacked Long Short-Term Memory (LSTM) models in classifying fitness posture images using detailed joint coordinate labeling. Design: Comparative analysis of machine learning models using a labeled dataset of fitness posture images. Methods: A dataset from AI-hub containing images and data of 41 exercises was used. Five exercises were selected and processed using a custom program. Data was converted from JSON to CSV format, augmented with joint condition information, and analyzed using Google Colab. Results: The best FNN model achieved a training error of 1.21% and test error of 9.08%. The Stacked LSTM model demonstrated superior performance with a training error of 1.05% and test error of 6.09%. Conclusion: Both FNN and Stacked LSTM models effectively classified sequential fitness images, with Stacked LSTM showing superior performance. This indicates the potential of Stacked LSTM models for accurate fitness posture classification in real-time coaching scenarios.

INTRODUCTION
    Our experimental data
    Datasets of Fitness Posture Image
    Definition of the exercise state
    Datasets of Fitness Posture Image
    Experimental Results
DISCUSSION
CONCLUSION
FUNDING
REFERENCES

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