Sign language recognition is one of the most challenging tasks of today’s era. Most of the researchers working in this domain have focused on different types of implementations for sign recognition. These implementations require the development of smart prototypes for capturing and classifying sign gestures. Keeping in mind the aspects of prototype design, sensor-based, vision-based, and hybrid approach-based prototypes have been designed. The authors in this paper have designed sensor-based assistive gloves to capture signs for the alphabet and digits. These signs are a small but important fraction of the ASL dictionary since they play an essential role in fingerspelling, which is a universal signed linguistic strategy for expressing personal names, technical terms, gaps in the lexicon, and emphasis. A scaled conjugate gradient-based back propagation algorithm is used to train a fully-connected neural network on a self-collected dataset of isolated static postures of digits, alphabetic, and alphanumeric characters. The authors also analyzed the impact of activation functions on the performance of neural networks. Successful implementation of the recognition network produced promising results for this small dataset of static gestures of digits, alphabetic, and alphanumeric characters ; publishedVersion


    Access

    Download


    Export, share and cite



    Title :

    Assistive Data Glove for Isolated Static Postures Recognition in American Sign Language Using Neural Network



    Publication date :

    2023-05-23


    Remarks:

    cristin:2148807
    1-13 ; 12 ; Electronics ; 8 ; 1904



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Template-based Recognition of Static Sitting Postures

    Zhu, Manli / Martinez, Aleix M. / Tan, Hong Z. | IEEE | 2003


    Gesture Recognition based on BP Neural Network and Data Glove

    Fu, Qiang / Fu, Jiajun / Guo, Jian et al. | British Library Conference Proceedings | 2020


    Static Human Body Postures Recognition in Video Sequences using the Belief Theory

    Girondel, V. / Bonnaud, L. / Caplier, A. et al. | British Library Conference Proceedings | 2005


    Static human body postures recognition in video sequences using the belief theory

    Girondel, V. / Bonnaud, L. / Caplier, A. et al. | IEEE | 2005


    Sign language recognition using competitive learning in the HAVNET neural network [3962-01]

    Sujan, V. A. / Meggiolaro, M. A. / International Society for Optical Engineering | British Library Conference Proceedings | 2000