• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1323-1329
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on the dynamic hand movement are used together. In this paper, we propose a hierarchical hand pose model based on finger position and shape for hand expression recognition. For hand pose recognition, a finger model representing the finger state and a hand pose model using the finger state are hierarchically constructed, which is based on the open source MediaPipe. The finger model is also hierarchically constructed using the bending of one finger and the touch of two fingers. The proposed model can be used for various applications of transmitting information through hands, and its usefulness was verified by applying it to number recognition in sign language. The proposed model is expected to have various applications in the user interface of computers other than sign language recognition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.15-16
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• 2021

본 논문에서는 RGB 영상 데이터를 입력으로 하여 mediapipe의 손 포즈 추정 알고리즘을 적용해 손가락 관절 및 주요 부위의 위치를 얻고 이를 기반으로 딥러닝 모델에 학습 후 손동작 인식 방법을 제안한다. 연속된 프레임에서 한 손의 손가락 주요 부위 간 좌표를 얻고 차분 벡터의 x, y좌표를 저장한 후 Conv1D, Bidirectional GRU, Transformer를 결합한 딥러닝 모델에 학습 후 손동작 인식 분류를 하였다. IC4You Gesture Dataset 의 한 손 동적 데이터 9개 클래스에 적용한 결과 99.63%의 손동작 인식 정확도를 얻었다.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.129-136
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• 2020

Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.7
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• pp.717-722
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• 2012

This paper presents the design and dynamic model of the finger exoskeleton actuated by Ionic Polymer Metal Composites (IPMC) to assist a tip pinch task. Although this exoskeleton will be developed to assist 3 degree-of-freedom motion of each finger, it has been currently made to perform the tip pinch task using 1 degree-of-freedom mechanism as the first step. The six layers of IPMC were stacked in parallel to increase the low actuation force of IPMC. In addition, the finger dummy was manufactured to evaluate the performance of the finger exoskeleton. The pinch task experiments, which were performed on the finger dummy with the developed exoskeleton, showed that the pinch force close to the desired level was obtained. Moreover, the dynamic model of the exoskeleton and finger dummy was developed in order to perform the various analyses for the improvement of the exoskeleton.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.657-682
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• 2011

This paper presents the dynamic analysis on the joint torque of a finger for the tip pinch task. The dynamic model on finger movement was developed in order to predict the joint torques of an index finger, and the finger was assumed as a three-link planar manipulator. Analysis of the model revealed that the joint stiffness was one of the most important parameters affecting the joint torque. The stiffness of the finger joint was experimentally measured, and it was used in analyzing the finger joint torque required for performing the tip pinch task. The obtained joint torque for the tip pinch task will be used as the design requirements of the finger exoskeletal orthosis actuated by the polymer actuator whose allowable torque limit is relatively low compared to that of a mechanical actuator.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1161-1170
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• 2016

In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.