A Deep Learning Perspective on Gym Leg Builder Workouts: Using CNN-RF for Better Classification

The research concentrates on five main types of exercise regimens, pertinently utilizing up-to-date machine learning methods to enhance activity determination accuracy and facility in the gym. The algorithm has noticed that the model got high accuracy levels by using both CNN to find features and R.F. for recognition, which shows such an option in terms of efficiency. Performance measures include Accuracy in identifying phrases correctly. They range from 93.47% to 94.94 %; Recall is the ability to identify all the correct words and ranges from 91.23% to 98.13%; and F1-scores range between 92.88% and 95.88%. The results of our model are 91.2% in total Accuracy. The next step was to add the confusion matrix as a disclosure, which gave me a more concrete knowledge of the contradictions concerning the model, especially the ones where misclassification occurred, mainly during operations with similar dynamics. Such outcomes indicate that one potentiality is unconsciously distinguishing the particular kinds of objects. This research suggests the significant effect that hybrid machine learning models accomplish in fitness technologies, sending out the most effective solutions that allow for 100% active training monitoring. Continuing work on this platform, I will consider increasing the number of input data samples, implementing real-time data processing, and fine-tuning model parameters to reach maximum classification accuracy. This research is a significant take-off step in the direction of utilizing artificial intelligence through the process of maintaining safe and efficient training settings in the fitness industry.