scholarly journals Deep Learning based Dynamic Hand Gesture Recognition with Leap Motion Controller

2020 ◽  
Vol 9 (5) ◽  
pp. 7309-7315
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Leap Motion ◽  
Motion Controller ◽  
Leap Motion Controller ◽  
Dynamic Hand Gesture Recognition

scholarly journals Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2106 ◽  
Author(s):  
Linchu Yang ◽  
Ji’an Chen ◽  
Weihang Zhu
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Recurrent Neural Network ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Leap Motion ◽  
Motion Controller ◽  
Hand Gestures ◽  
Leap Motion Controller ◽  
Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition is one of the most significant tools for human–computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion Controller (LMC) is proposed. In addition, based on LMC, an efficient way to capture the dynamic hand gestures is identified. Dynamic hand gestures are represented by sets of feature vectors from the LMC. The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively. On the ASL dataset with 360 samples, the system achieves accuracies of 100% and 96.3% on the training and testing sets. On the ASL dataset with 480 samples, the system achieves accuracies of 100% and 95.2%. On the Handicraft-Gesture dataset, the system achieves accuracies of 100% and 96.7%. In addition, 5-fold, 10-fold, and Leave-One-Out cross-validation are performed on these datasets. The accuracies are 93.33%, 94.1%, and 98.33% (360 samples), 93.75%, 93.5%, and 98.13% (480 samples), and 88.66%, 90%, and 92% on ASL and Handicraft-Gesture datasets, respectively. The developed system demonstrates similar or better performance compared to other approaches in the literature.

scholarly journals Implementation and Design User Interface Layout Use Leap Motion Controller With Hand Gesture Recognition

2019 ◽  
Vol 1339 ◽  
pp. 012058
Author(s):  
Billy Hendrik ◽  
Fauziah ◽  
Mardhiah Masril ◽  
Yunan Fauzi Wijaya ◽  
Silfia Andini
Keyword(s):  
User Interface ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Leap Motion ◽  
Motion Controller ◽  
Leap Motion Controller
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