| Title |
Hand Gesture Recognition using RVC Normalization and Transfer Learning |
| Authors |
김수열(Su-Yeol Kim) ; 김익진(Ik-Jin Kim) ; 이용찬(Yong-Chan Lee) ; 이연정(Yun-Jung Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.190 |
| Keywords |
Transfer Learning; Reference Voluntary Contraction; Hand Gesture Recognition; EMG; Ninapro DB |
| Abstract |
In this paper, we propose a new hand gesture recognition strategy using network-based transfer learning(TL) and reference voluntary contraction(RVC) normalization. The structure and parameters of the state-of-the-art deep learning models such as VGG19, ResNet152 and DenseNet121 for source task of image classification are reused in the target task of hand gesture recognition based on surface electromyography(EMG) signals. To mitigate the difficulty in handling the subject-dependent EMG signals, the RVC normalization is adopted in the signal pre-processing. The time-domain EMG signals are transformed into 2-D images for TL networks. The experimental results verify the validity of the proposed method in terms of recognition accuracy. The TL using VGG19, RVC normalization and gray image transformation shows 99.78% accuracy for the data from 15 participants performing 20 different gestures. |