Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource George, Geniets M.; Ummesalma, M.; Parihar, Ruchi Singh Title A name given to the resource Posture Classification Using a Hybrid Deep Learning Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IATMSI68868.2026.11465994" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IATMSI68868.2026.11465994</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036962349?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036962349?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George G.M., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Ummesalma M., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Parihar R.S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India Description An account of the resource Automated posture detection is a critical task in ergonomics and healthcare, yet it presents significant challenges for standard computer vision models, particularly in handling class imbalance and understanding geometric constraints. This paper proposes an enhanced hybrid deep learning model that synergizes the feature extraction power of a pre-trained ResNet50 architecture with engineered geometric features derived from the Radon Transform and pre-calculated joint angles. Our approach utilizes a dual-balancing strategy, combining data upsampling with a custom weighted loss function, to effectively address the problem of underrepresented classes. By processing visual and geometric data streams in parallel and fusing them within a deep architecture, our model achieves a holistic understanding of the subject's posture. The fine-tuned model demonstrates strong performance on an unseen test set, achieving a final accuracy of 92% for wrist posture and 92% for neck posture. Crucially, it attains a robust F1-score of 0.74 for the challenging 'Bad Wrist Posture' minority class, a significant improvement compared to the ResNet50-only baseline (F1=0.24) and achieves excellent ROC-AUC scores of 0.9859 for wrist and 0.9838 for neck, proving the efficacy of our hybrid, dual-balancing methodology for realworld application. 2026 IEEE. Subject The topic of the resource Class Imbalance; Deep Learning; Feature Engineering; Hybrid Model; Posture Detection; Radon Transform; ResNet50; Transfer Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154970-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online