• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.343-352
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• 2012

In this paper, we implemented a touchless interface for DID(Digital Information Display) system using gesture recognition technique which includes both hand motion and hand shape recognition. Especially this touchless interface without extra attachments gives user both easier usage and spatial convenience. For hand motion recognition, two hand-motion's parameters such as a slope and a velocity were measured as a direction-based recognition way. And extraction of hand area image utilizing YCbCr color model and several image processing methods were adopted to recognize a hand shape recognition. These recognition methods are combined to generate various commands, such as, next-page, previous-page, screen-up, screen-down and mouse -click in oder to control DID system. Finally, experimental results showed the performance of 93% command recognition rate which is enough to confirm the possible application to commercial products.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.122-122
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• 2018

1. 배경 최근 IoT 기술이 발전함에 따라 각종 전자기기에 들어가는 센서들이 점점 늘어나고 있다. 특히 사용자 중심의 기기들은 기술이 발전함에 따라 집적화가 이루어지면서, 하나의 기기에서 온도, 습도, 조도 등의 다양한 정보를 처리하고 있다. 이에 따라 더 많은 기능을 사용하기 위해, 소모 전력 또한 점차 증가하고 있다. 그러나 부피는 한정되어 있어, 기존 배터리만으로는 증가하는 소모 전력을 모두 보완하기 어렵다. 또한 대표적인 사용자 중심 기기인 스마트폰에서는, 가장 많은 전력을 소모하는 부분이 점점 커지고 있다. 이에 대한 대책으로 버려지는 에너지를 수확하여 전기적인 에너지로 바꿔주는 에너지 하베스팅 기술이 각광을 받고 있다. 에너지 하베스팅 기술은 바람, 진동, 인체의 움직임 등의 기계적 에너지, 태양광, 실내등의 빛 에너지를 전기적인 에너지로 바꿔주는 기술을 말한다. 본 연구에서는 강유전체 고분자 내부에 양자점이 임베딩된 박막을 이용하여, 스마트폰에서 발생하는 빛 에너지와 손가락으로 디스플레이를 터치할 때 발생하는 기계적인 에너지를 모두 수확할 수 있는 새로운 소자를 제시하였다. 소자 내부에 있는 양자점은 빛 에너지를 산란 혹은 흡수하여 발광한 후, 고분자 내부의 전반사를 통해 양 옆에 있는 태양전지로 빛을 전달한다. 또한 컴포짓의 매트릭스를 이루고 있는 강유전체 폴리머인 P(VDF-TrFE)는 강유전 특성을 통해 마찰전기 에너지를 효율적으로 전기 에너지로 전환할 수 있다. 강유전체 특성에 의해 P(VDF-TrFE) 내부에 정렬된 Polarization은 퀀텀닷에 양자구속 스타크 효과(Quantum Confined Stark Effect)를 일으켜 더 긴 파장을 방출한다. 이렇게 바뀐 파장은 실리콘 태양전지에서 더 많이 흡수할 수 있는 영역으로 방출되어 태양전지 출력의 증가를 일으킨다. 마지막으로 실리콘 태양전지의 출력 증가를 보여줌으로써 이를 실험적으로 입증했다.

• Journal of Biomedical Engineering Research
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• v.40 no.6
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.737-742
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• 2019

This paper is a study on the production of mixedeality contents by realizing AR coloring technology, virtual environment, and VR devices by realizing the interaction in virtual environment. In this study, we designed and produced the content from a fairy tale called 'Cinderella' to produce and demonstrate real mixed reality contents. 'Cinderella' shows 3D modeling in the virtual space using the Oculus Rift, and the modeled shoes can be lifted using Leap-Motion. It is a virtual augmented interaction content for exhibition and experience that can move a specific range from the foot on the treadmill. In this paper, we try to share the experience of creating new mixed reality game contents, which is a mixture of augmented reality and virtual reality technology, and continuation of universal use of this field.

• Journal of Platform Technology
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• v.11 no.1
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• pp.38-51
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• 2023

We propose an intelligent interface algorithm using hand gesture recognition information based on artificial intelligence. This method is functionally an interface that recognizes various motions quickly and intelligently by using MediaPipe and artificial intelligence techniques such as KNN, LSTM, and CNN to track and recognize user hand gestures. To evaluate the performance of the proposed algorithm, it is applied to a self-made 2D top-view racing game and robot control. As a result of applying the algorithm, it was possible to control various movements of the virtual object in the game in detail and robustly. And the result of applying the algorithm to the robot control in the real world, it was possible to control movement, stop, left turn, and right turn. In addition, by controlling the main character of the game and the robot in the real world at the same time, the optimized motion was implemented as an intelligent interface for controlling the coexistence space of virtual and real world. The proposed algorithm enables sophisticated control according to natural and intuitive characteristics using the body and fine movement recognition of fingers, and has the advantage of being skilled in a short period of time, so it can be used as basic data for developing intelligent user interfaces.