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@article{194538,
        author = {MRS. PAUL T JABA and DR. T. JAYA and MRS. P. PREETHI and MS. ASWINI J.P},
        title = {Hybrid Adaptive Neuro-Spatial Attention Framework (HANSAF) for Intelligent Clinical Motion Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5156-5163},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194538},
        abstract = {Understanding and classifying clinical movement patterns is becoming increasingly important in modern healthcare, especially in rehabilitation monitoring and skill-based training. However, accurately recognizing structured therapeutic movements remains challenging due to variations in posture, lighting conditions, camera angles, and background distractions. To address these limitations, this study introduces a Hybrid Adaptive Neuro-Spatial Attention Framework (HANSAF), a novel deep learning architecture designed to improve motion discrimination through adaptive feature prioritization. The proposed model combines a convolutional backbone with a spatial attention mechanism that dynamically highlights clinically meaningful joint movements while minimizing irrelevant visual noise. An adaptive fusion layer further refines the extracted representations to enhance class separability and stability during training. Experimental findings demonstrate improved convergence behavior, higher validation accuracy, and stronger generalization compared to conventional transfer learning approaches. The proposed framework offers a scalable and interpretable solution for intelligent clinical motion analysis and future healthcare AI systems.},
        keywords = {Clinical Motion Analysis, Deep Learning Framework, Spatial Attention Mechanism, Feature Optimization, Intelligent Healthcare Systems},
        month = {March},
        }