TY - GEN
T1 - 3D Hand Gesture Detection Using Deep Learning Algorithms
AU - Alnaim, Norah
AU - Elsharawy, Enas E.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 3D hand gesture recognition has significantly improved human-computer interactions due to the advancement in deep learning, depth sensors, and artificial intelligence. These technologies have enhanced the development of convenient, compatible hand recognition interfaces, thereby making it possible for people with disabilities to interact with others efficiently. The aim of this paper is to evaluate and compare 3D hand gestures using several metrics such as execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. Hence, this paper proposes the use of convolutional neural network (CNN) algorithms to recognize 3D hand gestures. Two people participated in the whole experiment, and a total of twelve 3D hand motions were recorded within a long distance. Experimental results show that the first participant in the single experiment and the second participant in the combined experiment have the best values in most parameters.
AB - 3D hand gesture recognition has significantly improved human-computer interactions due to the advancement in deep learning, depth sensors, and artificial intelligence. These technologies have enhanced the development of convenient, compatible hand recognition interfaces, thereby making it possible for people with disabilities to interact with others efficiently. The aim of this paper is to evaluate and compare 3D hand gestures using several metrics such as execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. Hence, this paper proposes the use of convolutional neural network (CNN) algorithms to recognize 3D hand gestures. Two people participated in the whole experiment, and a total of twelve 3D hand motions were recorded within a long distance. Experimental results show that the first participant in the single experiment and the second participant in the combined experiment have the best values in most parameters.
KW - CNN
KW - Convolutional Neural Network
KW - Deep Learning
KW - Gesture Recognition
KW - Hand Gesture Recognition
UR - https://www.scopus.com/pages/publications/105007528925
U2 - 10.1109/ICCIT63348.2025.10989332
DO - 10.1109/ICCIT63348.2025.10989332
M3 - Conference contribution
AN - SCOPUS:105007528925
T3 - Proceedings of 2025 4th International Conference on Computing and Information Technology, ICCIT 2025
SP - 340
EP - 347
BT - Proceedings of 2025 4th International Conference on Computing and Information Technology, ICCIT 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Computing and Information Technology, ICCIT 2025
Y2 - 13 April 2025 through 14 April 2025
ER -