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@article{208315,
        author = {Jafrin Sultana and Nowreen Afsana and Sumaiya Tabassum Dipty and Ishmam Fatima and Ferdous Karim Lucy and Md Ushama Shafoyat and Mahamudul Hasan Ashik},
        title = {Comprehensive Overview of Deep Neural Network Architectures for Gesture Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {1440-1461},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208315},
        abstract = {Hand gesture recognition (HGR) is a human–computer interaction (HCI) task that predicts the class and timing of a given hand movement. This comprehensive literature review examines state-of-the-art real-time hand gesture detection models employing deep learning. Deep learning algorithms have gained prominence in recent years due to their ability to automatically learn discriminative features from vast volumes of data. This paper will cover data acquisition methods, feature extraction, hand gesture classification, recently proposed applications, prominent environmental factors that affect accuracy, and a discussion of the performance of hand gesture recognition systems developed by various researchers over the past decade. Surface electromyography (sEMG) sensors integrated with wearable hand-gesture devices were the predominant acquisition modality in the reviewed studies. The review indicates that convolutional neural networks (CNNs) were the most frequently used classifiers and among the most effective approaches for gesture identification.},
        keywords = {Deep Learning, Hand Gesture Recognition, Human–Computer Interaction, Surface Electromyography (sEMG)},
        month = {September},
        }