• 대한인간공학회지
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• 제31권4호
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• pp.551-556
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• 2012

Objective: This study presents the effect of visual feedback on one-hand gesture performance in vision-based gesture recognition system when people use gestures to control a screen device remotely. Backgroud: gesture interaction receives growing attention because it uses advanced sensor technology and it allows users natural interaction using their own body motion. In generating motion, visual feedback has been to considered critical factor affect speed and accuracy. Method: three types of visual feedback(arrow, star, and animation) were selected and 20 gestures were listed. 12 participants perform each 20 gestures while given 3 types of visual feedback in turn. Results: People made longer hand trace and take longer time to make a gesture when they were given arrow shape feedback than star-shape feedback. The animation type feedback was most preferred. Conclusion: The type of visual feedback showed statistically significant effect on the length of hand trace, elapsed time, and speed of motion in performing a gesture. Application: This study could be applied to any device that needs visual feedback for device control. A big feedback generate shorter length of motion trace, less time, faster than smaller one when people performs gestures to control a device. So the big size of visual feedback would be recommended for a situation requiring fast actions. On the other hand, the smaller visual feedback would be recommended for a situation requiring elaborated actions.

• 대한인간공학회지
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• 제31권4호
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• pp.541-550
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• 2012

Objective: The aim of this study is to suggest a notation method for three-dimensional hand gesture. Background: To match intuitive gestures with commands of products, various studies have tried to derive gestures from users. In this case, various gestures for a command are derived due to various users' experience. Thus, organizing the gestures systematically and identifying similar pattern of them have become one of important issues. Method: Related studies about gesture taxonomy and notating sign language were investigated. Results: Through the literature review, a total of five elements of static gesture were selected, and a total of three forms of dynamic gesture were identified. Also temporal variability(reputation) was additionally selected. Conclusion: A notation method which follows a combination sequence of the gesture elements was suggested. Application: A notation method for three dimensional hand gestures might be used to describe and organize the user-defined gesture systematically.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• 한국멀티미디어학회논문지
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• 제16권12호
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• pp.1393-1402
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• 2013

Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

• 대한산업공학회지
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• 제41권5호
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• pp.461-469
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• 2015

The aim of this study is to find out the user's needs of a 3D hand gesture interface in the smart home environment. To find out the users' needs, we investigated which object the users want to use with a 3D hand gesture interface and why they want to use a 3D hand gesture interface. 3D hand gesture interfaces are studied to be applied to various devices in the smart environment. 3D hand gesture interfaces enable the users to control the smart environment with natural and intuitive hand gestures. With these advantages, finding out the user's needs of a 3D hand gesture interface would improve the user experience of a product. This study was conducted using a diary method to find out the user's needs with 20 participants. They wrote the needs of a 3D hand gesture interface during one week filling in the forms of a diary. The form of the diary is comprised of who, when, where, what and how to use a 3D hand gesture interface with each consisting of a usefulness score. A total of 322 data (209 normal data and 113 error data) were collected from users. There were some common objects which the users wanted to control with a 3D hand gesture interface and reasons why they want to use a 3D hand gesture interface. Among them, the users wanted to use a 3D hand gesture interface mostly to control the light, and to use a 3D hand gesture interface mostly to overcome hand restrictions. The results of this study would help develop effective and efficient studies of a 3D hand gesture interface giving valuable insights for the researchers and designers. In addition, this could be used for creating guidelines for 3D hand gesture interfaces.

• 로봇학회논문지
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• 제3권2호
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• pp.81-90
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• 2008

While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in $KAIST^[1]$. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user's hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.

• 한국산학기술학회논문지
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• 제14권10호
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• pp.5169-5175
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• 2013

본 논문에서는 삼차원 핸드 제스쳐 디자인 및 모델링을 위한 프레임워크를 기술한다. 동작 인식, 평가, 등록을 지원하기위해 동적시간정합(Dynamic Time Warping, 이하 DTW)과 은닉마코브모델 (Hidden Markov Mode, 이하 HMM)을 활용 하였다. HMM은 제스쳐 인식에 활용되며 또한 제스쳐 디자인과 등록 과정에 활용된다. DTW은 HMM 훈련 데이터가 부족한 경우 제스쳐 인식에 활용되고, 수행된 동작이 기준 동작의 차이를 평가하는 데에 활용된다. 동작 움직임에 나타나는 위치 정보와 관성 정보를 모두 획득하기 위해 바디센서와 시각센서를 혼합하여 동작을 감지하였다. 18개의 예제 손동작을 디자인하고 다양한 상황에서 제안된 기법을 테스트하였다. 또한 제스쳐 수행시 나타나는 사용자간 다양성에 대해 토론한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1106-1110
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• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• 제어로봇시스템학회논문지
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• 제8권7호
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• pp.599-606
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• 2002

A new method to govern the navigation of a mobile robot using hand gesture recognition is proposed based on the following two procedures. One is to achieve vision information by using a 2-DOF camera as a communicating medium between a man and a mobile robot and the other is to analyze and to control the mobile robot according to the recognized hand gesture commands. In the previous researches, mobile robots are passively to move through landmarks, beacons, etc. In this paper, to incorporate various changes of situation, a new control system that manages the dynamical navigation of mobile robot is proposed. Moreover, without any generally used expensive equipments or complex algorithms for hand gesture recognition, a reliable hand gesture recognition system is efficiently implemented to convey the human commands to the mobile robot with a few constraints.

• 한국위성정보통신학회논문지
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• 제9권1호
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• pp.57-64
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• 2014

최근 물리적 디바이스의 도움 없이 사람이 시스템과 인터랙션 할 수 있는 인간 친화적인 인간-기계 인터페이스가 많이 연구되고 있다. 이중 대표적인 것이 본 논문의 주제인 비전기반 제스처인식이다. 본 논문에서 우리는 설정된 가상세계의 객체와의 인터랙션을 위한 손 제스처들을 정의하고 이들을 인식할 수 있는 효과적인 방법론을 제안한다. 먼저, 웹캠으로 촬영된 저해상도 영상에서 사용자의 양손을 검출 및 추적하고, 손 영역을 분할하여 손 실루엣을 추출한다. 우리는 손 검출을 위해, RGB 공간에서 명암에 따라 두개의 타원형 모델을 이용하여 피부색을 모델링하였으며, 블랍매칭(blob matching) 방법을 이용하여 손 추적을 수행하였다. 우리는 플러드필(floodfill) 알고리즘을 이용해 얻은 손 실루엣의 행/열 모드 검출 및 분석을 통해 Thumb-Up, Palm, Cross 등 세 개의 손모양을 인식하였다. 그리고 인식된 손 모양과 손 움직임의 콘텍스트를 분석해서 다섯 가지 제스처를 인식할 수 있었다. 제안하는 제스처인식 방법론은 정확한 손 검출을 위해 카메라 앞에 주요 사용자가 한 명 등장한다는 가정을 하고 있으며 많은 실시간 데모를 통해 효율성 및 정확성이 입증되었다.