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

  • Authors

    • Swagatika Devi Department of CSE, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, ‎Bhubaneswar, India
    • , S. Rahmath Nisha Department of CSE, SRM Institute of Science and Technology, Tiruchirapalli, Tamil Nadu, India
    • Sarange Shreepad Marotrao Department of Mechanical Engineering, Ajeenkya D Y Patil School of Engineering, Pune, Maharashtra, India
    • Roopa R Department of CSE (Data Science), Madanapalle Institute of Technology & Science, Madanapalle, AP, India
    • B. Uma Maheswari Department of CSE, St. Joseph's College of Engineering, Chennai, Tamil Nadu, India
    • P. Murugeswari Department of AI and Data Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India.‎
    • P. John Augustine Department of IT, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
    • Sumit Chaudhary Department of CSE, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, India
    • H. Mickle Aancy Department of Master of Business Administration, Panimalar Engineering College, Chennai, Tamil Nadu, India
    • R. G. Vidhya Department of ECE, HKBK College of Engineering, Bangalore, India
    • Sreerekha.C. G Managing Director, Savvy mind research foundation, marthandam, India‎
    https://doi.org/10.14419/km6frv17

    Received date: June 28, 2025

    Accepted date: August 3, 2025

    Published date: August 8, 2025

  • Motion Capture Technology; Compact-Nanonet Deep Convolutional Neural Network (Cnanonet); Humboldt Squid ‎Optimization Algorithm (HSOA); Aero-bic Movement Analysis; Mean Curvature Flow.

  • Abstract

    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.

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    Devi , S. ., Nisha , , S. R. ., Marotrao , S. S. ., R, R. ., Maheswari , B. U. ., Murugeswari , P. ., Augustine, P. J. . ., Chaudhary , S. ., Aancy, H. M. ., Vidhya , R. G. ., & G, S. . (2025). A Deep Learning Framework for Human Motion RecognitionUsing ‎Compact CNNs and Swarm Optimization. International Journal of Basic and Applied Sciences, 14(4), 211-219. https://doi.org/10.14419/km6frv17