Next Article in Journal
Single-Coil Eddy Current Sensors and Their Application for Monitoring the Dangerous States of Gas-Turbine Engines
Next Article in Special Issue
A Method for Sensor-Based Activity Recognition in Missing Data Scenario
Previous Article in Journal
Deep Learning to Unveil Correlations between Urban Landscape and Population Health
Article

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

1
Department of Mechanical Engineering; Jiangsu University of Science and Technology, Zhenjiang 212003, China
2
Department of Engineering Technology; University of Houston, Houston, TX 77204, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2106; https://doi.org/10.3390/s20072106
Received: 28 February 2020 / Revised: 1 April 2020 / Accepted: 6 April 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Sensors for Activity 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. View Full-Text
Keywords: hand gesture recognition; leap motion controller (LMC); recurrent neural network (RNN) hand gesture recognition; leap motion controller (LMC); recurrent neural network (RNN)
Show Figures

Figure 1

MDPI and ACS Style

Yang, L.; Chen, J.; Zhu, W. Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network. Sensors 2020, 20, 2106. https://doi.org/10.3390/s20072106

AMA Style

Yang L, Chen J, Zhu W. Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network. Sensors. 2020; 20(7):2106. https://doi.org/10.3390/s20072106

Chicago/Turabian Style

Yang, Linchu, Ji’an Chen, and Weihang Zhu. 2020. "Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network" Sensors 20, no. 7: 2106. https://doi.org/10.3390/s20072106

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop