Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare  

作　　者：Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar 

机构地区：[1]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,P.O.Box 51178,Riyadh,11543,Kingdom of Saudi Arabia [2]Department of Computer Engineering,College of Computing and Informatics,University of Sharjah,Sharjah,27272,United Arab Emirates [3]Laboratory Biomechanics and Bioengineering,University of Technology of Compiegne,Compiegne,60200,France [4]Department of Computer Science and Engineering,Manipal University Jaipur,Jaipur,303007,India

出　　处：《Computers, Materials & Continua》2024年第1期127-144,共18页计算机、材料和连续体（英文）

基　　金：funded by Researchers Supporting Project Number(RSPD2024 R947),King Saud University,Riyadh,Saudi Arabia.

摘　　要：Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关 键 词：Computer vision deep learning gait recognition sign language recognition machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]