TY - GEN
T1 - A Cloud-Based 3D Digital Twin for Arabic Sign Language Alphabet Using Machine Learning Object Detection Model
AU - Abduljabbar, Mohammed
AU - Gochoo, Munkhjargal
AU - Sultan, Mohamad T.
AU - Batnasan, Ganzorig
AU - Otgonbold, Munkh Erdene
AU - Berengueres, Jose
AU - Alnajjar, Fady
AU - Rasheed, Ashika Abdul
AU - Alshamsi, Ahmed
AU - Alsaedi, Naser
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - People with hearing loss or hard hearing struggle with daily life activities as sign language is not widely known by the public. There are many attempts to use technology to help assist hearing loss individuals. However, most proposed solutions are standalone applications or require special hardware like a wearable glove. Our goal is to leverage cloud computing and artificial intelligence (AI) to provide a solution that is portable and does not require any special hardware. We created a lightweight 3D model and rendered it on the browser along with another lightweight object detection model for Arabic Sign Language (ArSL) for real-Time detection. Our contribution is primarily based on integrating our novel functional lightweight 3D avatar model and a lightweight ArSL alphabet detection model, which is trained on public ArSL21L dataset, that are suitable to be given as a cloud service. Prototypes of the 3D digital twin avatar model and AI model are publicly offered for the research community on GitHub. We will be working on a full-scale real-Time cloud-based communication system in ArSL.
AB - People with hearing loss or hard hearing struggle with daily life activities as sign language is not widely known by the public. There are many attempts to use technology to help assist hearing loss individuals. However, most proposed solutions are standalone applications or require special hardware like a wearable glove. Our goal is to leverage cloud computing and artificial intelligence (AI) to provide a solution that is portable and does not require any special hardware. We created a lightweight 3D model and rendered it on the browser along with another lightweight object detection model for Arabic Sign Language (ArSL) for real-Time detection. Our contribution is primarily based on integrating our novel functional lightweight 3D avatar model and a lightweight ArSL alphabet detection model, which is trained on public ArSL21L dataset, that are suitable to be given as a cloud service. Prototypes of the 3D digital twin avatar model and AI model are publicly offered for the research community on GitHub. We will be working on a full-scale real-Time cloud-based communication system in ArSL.
KW - 3D Avatar
KW - Digital Twin
KW - Meta AI
KW - Neural Network
KW - Sign Language
UR - http://www.scopus.com/inward/record.url?scp=85182945107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182945107&partnerID=8YFLogxK
U2 - 10.1109/IIT59782.2023.10366491
DO - 10.1109/IIT59782.2023.10366491
M3 - Conference contribution
AN - SCOPUS:85182945107
T3 - 2023 15th International Conference on Innovations in Information Technology, IIT 2023
SP - 208
EP - 212
BT - 2023 15th International Conference on Innovations in Information Technology, IIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Innovations in Information Technology, IIT 2023
Y2 - 14 November 2023 through 15 November 2023
ER -