Enhancement of surgical hand gesture recognition using a capsule network for a contactless interface in the operating room
| Autor: | Namkug Kim, Areum Lee, Yongwon Cho, Seongho Jin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Operating Rooms Computer science Interface (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Health Informatics Convolutional neural network 030218 nuclear medicine & medical imaging Pattern Recognition Automated 03 medical and health sciences User-Computer Interface Young Adult 0302 clinical medicine Humans Computer vision Artificial neural network Gestures business.industry Deep learning Middle Aged Frame rate Hand Computer Science Applications Surgery Computer-Assisted Gesture recognition Female Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery Algorithms Software Gesture |
| Zdroj: | Computer methods and programs in biomedicine. 190 |
| ISSN: | 1872-7565 |
| Popis: | Background and objective: Hand gesture recognition systems in operating rooms (ORs) are crucial for browsing and controlling computer-aided devices, which have been developed to decrease the risk of contamination during surgical procedures. Methods: We proposed the use of hand gesture recognition to enhance accuracies and recognition areas with the capsule network (CapsNet) of deep neural network and Leap Motionâ Our method includes the i) extraction and preprocessing of infrared (IR) images (60 frames per second) from Leap Motion™, ii) training of various types of networks, and iii) gesture recognition evaluation in the OR. We trained the images of training dataset (N=903) and tested images (N=100) using five types of surgical hand gestures including hovering, grab, click, one peak, and two peaks by 10 subjects with various types of augmentation methods including rotate (0∘, 90∘, 180∘), scale, translation, illumination, and resize. Results: CapsNet achieved a classification accuracy of 86.46% (around 10% improvement) compared with 73.67% for the baseline convolutional neural network (CNN) and 76.4% for VGG16. Conclusions: In conclusion, the accuracy of hand gesture recognition with CapsNet was better than that of conventional CNNs, which could be used to navigate and manipulate various types of computer-aided devices and applications through contactless gesture interaction. |
| Databáze: | OpenAIRE |
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