A Deep Learning Framework for Human Motion RecognitionUsing Compact CNNs and Swarm Optimization

Aerobic exercise is essential to maintain the cardiovascular system's health, yet accurate analysis of its ‎complex motions poses severe challenges. Traditional methods often encounter computational loads, overfitting, ‎and unfavorable generalization in real applications. In trying to resolve these issues, this work introduces the MCT-‎MCT-MCT-MCT-MCT-MCT-MCT-HCNanoNet-AM framework, which combines motion capture technology (MCT) and a Humboldt Squid ‎Optimization Algorithm (HSOA)-optimized Compact-NanoNet Deep Convolutional Neural Network (CNanoNet). ‎This blended approach is going to enhance the accuracy and efficiency of aerobic movement analysis. The MCT-‎MCT-MCT-MCT-MCT-MCT-MCT-HCNanoNet-AM system workflow incorporates several key steps. Initially, high-speed motion capture technology ‎is utilized to capture data. The raw data are processed beforehand using Mean Curvature Flow, which is a technique ‎to remove noise at the first step. Afterwards, feature extraction and movement detection are done using the ‎CNanoNet model. The HSOA is then employed to optimize the CNanoNet for improving model performance. The ‎optimized model is then utilized for real-time aerobic movement recognition with personalized feedback to the ‎users. Experimental results indicate that the MCT-HCNanoNet-AM system noticeably enhances the recognition ‎and classification of aerobic movements with an exceptional set of 99.98% accuracy, 99.99% precision, 99.986% ‎specificity, 99.99% recall, and 99.98% F-Score. The system takes 91 seconds in computation and area under the ‎curve (AUC) of 0.9976. Integration of real-time feedback mechanisms not only helps in refining the techniques of ‎users but also in the prevention of injuries by identifying potential risks. Overall, the MCT-HCNanoNet-AM system ‎is a major advance in aerobic movement recognition technology that enhances performance as well as overall ‎physical well-being through innovative technological advancements‎.