Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Models in Surgery
In this paper, we present a novel touchless interaction system for visualization of hepatic anatomical models in surgery. Real-time visualization is important in surgery, particularly during the operation. However, it often faces the challenge of efficiently reviewing the patient’s 3D anatomy model while maintaining a sterile field. The touchless technology is an attractive and potential solution to address the above problem. We use a Microsoft Kinect sensor as input device to produce depth images for extracting a hand without markers. Based on this representation, a deep convolutional neural network is used to recognize various hand gestures. Experimental results demonstrate that our system can significantly improve the response time while achieve almost same accuracy compared with the previous researches.
KeywordsDeep learning Kinect Real-time touch-less interaction Visualization of hepatic anatomical models
Authors would like to thank Dr. M. Kaibori of KANSAI Medical University for providing medical images and advice on surgical support systems. This work is supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant Nos. 16H01436, 15K16031, 17H00754, 17K00420, 18H03267; in part by the MEXT Support Program for the Strategic Research Foundation at Private Universities, Grant (2013–2017).
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