• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.627-636
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• 2011

The purpose of this study is to develop the multi-modal interaction system. This system provides realistic and an immersive experience through multi-modal interaction. The system recognizes user behavior, intention, and attention, which overcomes the limitations of uni-modal interaction. The multi-modal interaction system is based upon gesture interaction methods, intuitive gesture interaction and attention evaluation technology. The gesture interaction methods were based on the sensors that were selected to analyze the accuracy of the 3-D gesture recognition technology using meta-analysis. The elements of intuitive gesture interaction were reflected through the results of experiments. The attention evaluation technology was developed by the physiological signal analysis. This system is divided into 3 modules; a motion cognitive system, an eye gaze detecting system, and a bio-reaction sensing system. The first module is the motion cognitive system which uses the accelerator sensor and flexible sensors to recognize hand and finger movements of the user. The second module is an eye gaze detecting system that detects pupil movements and reactions. The final module consists of a bio-reaction sensing system or attention evaluating system which tracks cardiovascular and skin temperature reactions. This study will be used for the development of realistic digital entertainment technology.

• The Journal of Korea Robotics Society
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• v.4 no.2
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• pp.139-146
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• 2009

This paper describes how a person extracts a unknown object with pointing gesture while interacting with a robot. Using a stereo vision sensor, our proposed method consists of two stages: the detection of the operators' face, the estimation of the pointing direction, and the extraction of the pointed object. The operator's face is recognized by using the Haar-like features. And then we estimate the 3D pointing direction from the shoulder-to-hand line. Finally, we segment an unknown object from 3D point clouds in estimated region of interest. On the basis of this proposed method, we implemented an object registration system with our mobile robot and obtained reliable experimental results.

• Korean Journal of Computational Design and Engineering
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• v.21 no.2
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• pp.159-169
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• 2016

In order to realize the virtual prototyping (VP) of digital products, it is important to provide the people involved in product development with the appropriate visualization and interaction of the products, and the vivid simulation of user interface (UI) behaviors in an interactive 3D virtual environment. In this paper, we propose an approach to web-based 3D virtual experience using Unity and Leap Motion. We adopt Unity as an implementation platform which easily and rapidly implements the visualization of the products and the design and simulation of their UI behaviors, and allows remote users to get an easy access to the virtual environment. Additionally, we combine Leap Motion with Unity to embody natural and immersive interaction using the user's hand gesture. Based on the proposed approach, we have developed a testbed system for web-based 3D virtual experience and applied it for the design evaluation of various digital products. Button selection test was done to investigate the quality of the interaction using Leap Motion, and a preliminary user study was also performed to show the usefulness of the proposed approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1088-1091
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• 2017

최근 그래픽스 기반의 가상현실 기술의 발전과 관심이 증가하면서 3D 객체와의 자연스러운 상호작용을 위한 방법들 중 손 제스처 인식에 대한 연구가 활발히 진행되고 있다. 본 논문은 가상현실 3D 오브젝트와의 상호작용을 위한 MCSVM 기반의 손 제스처 인식을 제안한다. 먼저 다양한 손 제스처들을 립모션을 통해 입력 받아 전처리를 수행한 손 데이터를 전달한다. 그 후 이진 결정 트리로 1차 분류를 한 손 데이터를 리샘플링 한 뒤 체인코드를 생성하고 이에 대한 히스토그램으로 특징 데이터를 구성한다. 이를 기반으로 MCSVM 학습을 통해 2차 분류를 수행하여 제스처를 인식한다. 실험 결과 3D 오브젝트와 상호작용을 위한 16개의 명령 제스처에 대해 평균 99.2%의 인식률을 보였고 마우스 인터페이스와 비교한 정서적 평가 결과에서는 마우스 입력에 비하여 직관적이고 사용자 친화적인 상호작용이 가능하다는 점에서 게임, 학습 시뮬레이션, 설계, 의료분야 등 많은 가상현실 응용 분야에서의 입력 인터페이스로 활용 될 수 있고 가상현실에서 몰입도를 높이는데 도움이 됨을 알 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.421-425
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• 2006

최근 들어서 휴머노이드 로봇을 비롯한 로봇에 대하여 관심이 증대되고 있다. 이에 따라, 외모를 닮은 로봇 뿐 만 아니라, 사람과 상호 작용을 할 수 있는 로봇 기술의 중요성이 부각되고 있다. 이러한 상호 작용을 위한 효율적이고, 가장 자연스러운 방법 중의 하나가 비전을 기반으로 한 제스처 인식이다. 제스처를 인식하는데 있어서 가장 중요한 것은 손의 모양과 움직임을 인식하는3차원 제스처 인식이다. 본 논문에서는 3차원 손 제스처를 인식하기 위하여3차원 손 모델 추정 방법과 명령형 제스처 인식 시스템을 소개하고, 수화, 지화 등으로의 확장성을 위한 프레임워크를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.459-462
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• 2007

본 연구에서는 동적 수신호 인식문제를 위하여 CNN 모델을 사용한 특징추출 기법과, FMM 신경망을 사용한 특징 분석 기법을 상호 결합한 형태의 패턴 인식 모델을 제안한다. 수신호 인식을 위하여 영상패턴에서 대상물의 움직임 정보에 기초한 3 차원 형식의 데이터 표현 기법과, 이로부터 인식을 위한 특징추출 기법을 제시한다. 특징추출 모듈에서는 3 차원으로 확장된 구조의 수용영역을 고려한 CNN 모델을 제안하며, 이로부터 학습패턴에서 특징점의 공간적 변이에 대한 영향을 최소화할 수 있음을 고찰한다. 또한 인식효율의 개선을 위하여 방대한 양의 특징집합으로부터 효과적인 특징을 선별하기 위한 방법론으로서 WFMM 모델 기반의 특징분석 기법을 정의하고 이로부터 선별된 특징을 사용하는 인식 기법을 소개한다.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.411-417
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• 2009

We proposed a robust body part tracking system for intelligent environment that will not limit freedom of users. Unlike any previous gesture recognizer, we upgraded the generality of the system by creating the ability the ability to recognize details, such as, the ability to detect the difference between long sleeves and short sleeves. For the precise each body part tracking, we obtained the image of hands, head, and feet separately from a single camera, and when detecting each body part, we separately chose the appropriate feature for certain parts. Using a calibrated camera, we transferred 2D detected body parts into the 3D posture. In the experimentation, this system showed advanced hand tracking performance in real time(50fps).

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.619-628
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• 2011

Normally vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment. On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.685-691
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• 2020

In this study, an algorithm that recognizes numbers from 0 to 9 was developed using the data obtained after tracking hand movements using the echo signal of a millimeter-wave radar sensor at 77 GHz. The echo signals obtained from the radar sensor by detecting the motion of a hand gesture revealed a cluster of irregular dots due to the difference in scattering cross-sectional area. A valid center point was obtained from them by applying a K-Means algorithm using 3D coordinate values. In addition, the obtained center points were connected to produce a numeric image. The recognition rate was compared by inputting the obtained image and an image similar to human handwriting by applying the smoothing technique to a CNN (Convolutional Neural Network) model trained with MNIST (Modified National Institute of Standards and Technology database). The experiment was conducted in two ways. First, in the recognition experiments using images with and without smoothing, average recognition rates of 77.0% and 81.0% were obtained, respectively. In the experiment of the CNN model with augmentation of learning data, a recognition rate of 97.5% and 99.0% on average was obtained in the recognition experiment using the image with and without smoothing technique, respectively. This study can be applied to various non-contact recognition technologies using radar sensors.