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KMID : 1137820210420040186
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의공학회지
2021 Volume.42 No. 4 p.186 ~ p.192
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Hand Gesture Recognition with Convolution Neural Networks for Augmented Reality Cognitive Rehabilitation System Based on Leap Motion Controller
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Song Keun-San
Lee Hyun-Ju
Tae Ki-Sik
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Abstract
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In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.
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KEYWORD
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Euler angle spectrograph, Convolutional neural network (CNN), Hand gesture recognition, Augmented reality (AR), Cognitive rehabilitation system, Leap motion controller (LMC)
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