Devising a Textual Analogous Computation-Centric Approach for Profiling Exercise Levels

This paper introduces the Textual Analogous Computation-Centric Approach (TACCA) for textual data-based exercise level monitoring. TACCA uses natural language processing and machine learning to classify written exercise reports by difficulty to address concerns with smart devices and self-reported surveys. Semantic analysis and word embeddings extract textual characteristics. Machine learning algorithms use these properties to categorize exercise levels in a labelled dataset. Memory, accuracy, and Area Under the Curve (AUC) compare TACCA to standard research methods. The results show that TACCA accurately measures exercise intensity better than other methods. TACCA distinguishes exercise types and reduces classification mistakes with 0.85 accuracy, 0.87 precision, 0.83 recall, and 0.92 AUC. This research's ablation study separates TACCA's key components. It shows how powerful machine learning and semantic analysis raise performance standards. These results show that TACCA's general design approach works when components interact to improve prediction accuracy and flexibility. Healthcare systems may employ TACCA due to its fast and large processor. TACCA can handle large data sets and function in various healthcare situations, making it ideal for health and fitness monitoring.