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김수열(Su-Yeol Kim),김익진(Ik-Jin Kim),이용찬(Yong-Chan Lee),이연정(Yun-Jung Lee) 대한전기학회 2021 전기학회논문지 Vol.70 No.1
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.
이수열,이성근,이정한,Lee, Su-Yeol,Lee, Seong-Geun,Lee, Jeong-Han 대한의용생체공학회 1995 의공학회지 Vol.16 No.3
An implementation scheme of the magnetic nerve stimulator using a switching mode power supply is proposed. By using a switching mode power supply rather than a conventional linear power supply for chArging high voltage cApacitors, the weight and size of the magnetic net've stimulator can be considerably reduced. Maximum output voltage of the developed magnetic nerve stimulator using the switching mode power supply is 3,000 volts and switching time is about 100 msec Experimental results of human nerve stimulations using the developed stimulator are presented.