TY - GEN
T1 - Hand Gesture Recognition with Augmented Reality and Leap Motion Controller
AU - Huo, Jiage
AU - Keung, K. L.
AU - Lee, C. K.M.
AU - Ng, H. Y.
N1 - Funding Information:
This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP2-2), Innovation and Technology Fund, Hong Kong, China. The authors also would like to thank the support of Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - The use of hand gestures is one of the commonly used communication approaches in human daily life, especially for the deaf and dumb. Hand gesture recognition can be adopted in human-computer interaction for converting hand gestures into words or sentences. Unfortunately, the same gesture may have diverse meanings in different countries. With the aim of eliminating the communication barriers between hearing-impaired communities and the general people, an efficient interaction user interface created with the augmented reality technique and leap motion controller for hand gesture recognition and translation is proposed in this paper. Five hand gestures captured by a leap motion controller were used for learning and recognizing through machine learning methodologies, including Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, Deep Neural Network and Decision Tree. The experimental results from different classifiers reveal the practicability of employing hand gesture recognition in text translation. The hand gesture recognition system should be capable of reducing the communication gap between hearing disabilities and the public so as to avoid deaf and mute people being isolated from society.
AB - The use of hand gestures is one of the commonly used communication approaches in human daily life, especially for the deaf and dumb. Hand gesture recognition can be adopted in human-computer interaction for converting hand gestures into words or sentences. Unfortunately, the same gesture may have diverse meanings in different countries. With the aim of eliminating the communication barriers between hearing-impaired communities and the general people, an efficient interaction user interface created with the augmented reality technique and leap motion controller for hand gesture recognition and translation is proposed in this paper. Five hand gestures captured by a leap motion controller were used for learning and recognizing through machine learning methodologies, including Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, Deep Neural Network and Decision Tree. The experimental results from different classifiers reveal the practicability of employing hand gesture recognition in text translation. The hand gesture recognition system should be capable of reducing the communication gap between hearing disabilities and the public so as to avoid deaf and mute people being isolated from society.
KW - Hand gesture recognition
KW - Leap motion controller
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85125366225
U2 - 10.1109/IEEM50564.2021.9672611
DO - 10.1109/IEEM50564.2021.9672611
M3 - Conference article published in proceeding or book
AN - SCOPUS:85125366225
T3 - 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
SP - 1015
EP - 1019
BT - 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021
Y2 - 13 December 2021 through 16 December 2021
ER -