Deep gesture interaction for augmented anatomy learning

Ahmad Karambakhsh, Aouaidjia Kamel, Bin Sheng, Ping Li, Po Yang, David Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)328-336
Number of pages9
JournalInternational Journal of Information Management
Volume45
DOIs
Publication statusPublished - Apr 2019
Externally publishedYes

Keywords

  • 3D reconstruction
  • Augmented reality
  • Medical education
  • Mobile cloud
  • Neural network

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Library and Information Sciences

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