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Title: Smart training : mask R-CNN oriented approach
Authors: Su, MC
Chen, JH
Trisandini, Azzizi, V
Chang, HL
Wei, HH 
Issue Date: Dec-2021
Source: Expert systems with applications, 15 Dec. 2021, v. 185, 115595
Abstract: This paper is aimed at the usage of an augmented reality assisted system set up on the smart-glasses for training activities. Literature review leads us to a comparison among related technologies, yielding that Mask Regions with Convolutional Neural Network (R-CNN) oriented approach fits the study needs. The proposed method including (1) pointing gesture capture, (2) finger-pointing analysis, and (3) virtual tool positioning and rotation angle are developed. Results show that the recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different lighting, such as indoor and outdoor, the pointing analysis accuracy is up to 79%. The error between the analysis angle and the actual angle is only 1.32 degrees. The results proved that the system is well suited to present the effect of augmented reality, making it applicable for real world usage.
Keywords: Augmented reality
Finger-pointing analysis
Hand gesture recognition
Mask Regions with Convolutional Neural Network (R-CNN)
Smart training
Publisher: Pergamon Press
Journal: Expert systems with applications 
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2021.115595
Rights: © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (
The following publication Su, M. C., Chen, J. H., Azzizi, V. T., Chang, H. L., & Wei, H. H. (2021). Smart training: Mask R-CNN oriented approach. Expert Systems with Applications, 185, 115595 is available at
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