• 대한임베디드공학회논문지
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• 제17권4호
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• pp.229-237
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• 2022

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (C)
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.517-521
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• 2008

Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion' s directions by its orientation. We manage the flow orientation histogram as motion descriptor. A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.

• 전기전자학회논문지
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• 제26권1호
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• pp.133-136
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• 2022

This paper is about motion gesture recognition system, and proposes the following improvement to the flaws of the current system: a motion gesture recognition system and such algorithm that uses the video image of the entire hand and reading its motion gesture to advance the accuracy of recognition. The motion gesture recognition system includes, an image capturing unit that captures and obtains the images of the area applicable for gesture reading, a motion extraction unit that extracts the motion area of the image, and a hand gesture recognition unit that read the motion gestures of the extracted area. The proposed application of the motion gesture algorithm achieves 20% improvement compared to that of the current system.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.186-192
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• 2021

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.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.324-329
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• 2009

Proactive computing의 핵심 기술인 손동작 인식 (Hand Motion Recognition, HMR) 기술은 인간과 컴퓨터 사이의 상호작용(Human Computer Interaction, HCI) 분야에서 많은 연구가 진행되고 있다. 본 연구에서는 3축 가속도 센서를 부착한 data glove를 제작하고, 3차원 손 모델을 구현한 후, 이를 이용한 손동작 인식 기술을 개발하였다. Data glove는 가상현실에 대한 입력 장치로써 본 논문에서는 3축 가속도 센서를 사용하여 획득된 신호를 wireless communication으로 PC에 전송할 수 있도록 구현하였다. 손 모델링은 ellipsoid를 이용한 kinematic chain 이론 바탕의 3차원 손 모델을 구현하였으며, data glove에서 얻어진 가속도 정보에 rule 기반의 알고리즘을 적용하여 구현된 3차원 손 모델을 통하여 간단한 손동작(가위, 바위, 보)을 인식하였다.

• 제어로봇시스템학회논문지
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• 제17권4호
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

• 정보처리학회논문지B
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• 제18B권3호
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• pp.109-116
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• 2011

본 논문에서는 기계적인 조작 장치 없이 손동작만으로 컴퓨터를 조작할 수 있는 차세대 인터페이스인 에어 인터페이스를 구현하였다. 에어 인터페이스 시스템 구현을 위해 먼저 적외선의 전반사 원리를 이용하였으며, 이후 획득된 적외선 영상에서 손 영역을 분할한다. 매 프레임에서 분할된 손 영역은 이벤트 처리를 위한 손동작 인식부의 입력으로 사용되고, 최종적으로 개별 제어 이벤트에 맵핑된 손동작 인식을 통하여 일반적인 제어를 수행하게 된다. 본 연구에서는 손영역 검출과 추적, 손동작 인식과정을 위해 구현되어진 영상처리 및 인식 기법들이 소개되며, 개발된 에어 인터페이스 시스템은 길거리 광고, 프레젠테이션, 키오스크 등의 그 활용성이 매우 클 것으로 기대된다.

• 디지털콘텐츠학회 논문지
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• 제13권3호
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• pp.343-352
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• 2012

본 논문에서는 DID 시스템에서 사용할 수 있는 제스쳐 인식을 이용한 비접촉식 인터페이스를 구현하였다. 비접촉식 인터페이스는 별도의 부착물 없이 키넥트 카메라만을 사용함으로, 사용자의 편의와 공간적인 활용도를 높일 수 있다. 손 동작인식에는 사용자의 손 움직임의 기울기와 속력을 인식하는 방향성 기반의 인식 기법을 채용하였고 손 모양인식을 위해서 YCbCr 칼라모델을 이용한 손 영역 추출과 손 넓이의 원을 이용한 영상처리 기술로 손가락의 수를 인식하였다. 이러한 손 동작인식과 손 모양인식을 이용하여 다음 페이지, 이전 페이지, 화면 위로, 화면 아래로, 커서 움직임, 클릭 등의 이벤트를 발생시켜 DID 시스템 제어 명령으로 사용하였으며, 구현한 시스템을 갖고 동작 실험한 결과 93%의 명령 인식률을 보여 실용화의 가능성을 확인할 수 있었다.

• 제어로봇시스템학회논문지
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• 제21권12호
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• pp.1100-1108
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• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.