Implementing Convolutional Neural Networks for Automated Disease Diagnosis in Telemedicine

This research introduces a computer-based strategy for defining exercise levels to improve existing methods for properly expressing physical activity patterns. We use NLP, Word Embedding, CNN, and Gradient Boosting methods to group exercise data using sophisticated machine learning and language analysis. An analysis of accuracy, precision, memory, and F1 score shows that the recommended strategy outperforms heart rate monitoring and step counts. We also conduct ablation studies to thoroughly evaluate how each algorithm enhances the performance of the recommended method, highlighting their importance. The results indicate the need for a more thorough approach to activity level monitoring that makes use of both traditional and technological methods to get insight into individuals' exercise regimens. The system may be used for fitness tracking, sports statistics, and medical purposes. The data aids in the development of customised treatment plans, enhances performance, and guards against injuries.