AI-Driven Exercise Monitoring for Non-Surgical Obesity Treatment: A Deep Learning-Based Posture and Movement Optimization System

• Shwetambari Korde

  Author

Non-surgical obesity treatment represents a critical intervention strategy for global health management, with structured physical activity serving as a fundamental component for effective weight reduction and metabolic health improvement. However, the efficacy of exercise-based interventions is frequently compromised by improper execution, lack of real-time feedback, and insufficient personalization, leading to diminished results and increased injury risks. This research introduces an advanced artificial intelligence-powered exercise evaluation framework specifically designed to optimize obesity-focused physical activity regimens through sophisticated deep learning-based movement analysis and instantaneous posture correction. The proposed system employs Spatio-Temporal Polychromatic Trajectory (STPT) imaging alongside pose estimation algorithms to monitor and assess weight management exercises including cardiovascular routines, resistance training, and functional movements. Utilizing Convolutional Neural Networks (CNNs), the framework identifies movement patterns, evaluates exercise quality, and delivers AI-driven corrective feedback to enhance workout efficiency and safety. Designed for both home-based and clinical deployment, the system integrates with mobile platforms and wearable devices to provide personalized exercise recommendations tailored to individual weight loss objectives. Extensive experimental validation demonstrates that the proposed system achieves classification accuracy exceeding 90\% across multiple exercise modalities, establishing its effectiveness as a reliable tool for non-surgical obesity intervention programs. This study underscores the transformative potential of AI-enhanced fitness monitoring in supporting personalized weight management strategies, improving exercise adherence, and minimizing injury occurrence in obesity treatment protocols.

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