Journal
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
Volume 45, Issue -, Pages 328-336Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijinfomgt.2018.03.004
Keywords
Neural network; Augmented reality; 3D reconstruction; Medical education; Mobile cloud
Categories
Funding
- National Natural Science Foundation of China [61572316, 61671290]
- National High-tech R&D Program of China (863 Program) [2015AA015904]
- Key Program for International S&T Cooperation Project of China [2016YFE0129500]
- Science and Technology Commission of Shanghai Municipality [16DZ0501100, 17411952600]
- Interdisciplinary Program of Shanghai Jiao Tong University [14JCY10]
- Research Grants Council of Hong Kong [28200215]
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Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems.
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