• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.241-246
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• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.129-137
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• 2013

As the usage of computer based systems continues to increase in our normal life, there are constant efforts to enhance the accessibility of information for handicapped people. For this, it is essential to develop new interface ways for physical disabled peoples by means of human-computer interface (HCI) or human-machine interface (HMI). In this paper, we developed HMI using electromyogram (EMG) and electrooculogram (EOG) for people with physical disabilities. Developed system is composed of two modules, hardware module for signal sensing and software module for feature extraction and pattern classification. To maximize ease of use, only two skin contact electrodes are attached on both ends of brow, and EOG and EMG are measured simultaneously through these two electrodes. From measured signal, nine kinds of command patterns are extracted and defined using signal processing and pattern classification method. Through Java based real-time monitoring program, developed system showed 92.52% of command recognition rate. In addition, to show the capability of the developed system on real applications, five different types of commands are used to control ER1 robot. The results show that developed system can be applied to disabled person with quadriplegia as a novel interface way.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.563-570
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• 2006

본 논문은 감정 교류를 위한 목적으로 가상 캐릭터에서 사용되기 시작하여 현재 로봇에 이르기까지 그 이용 범위를 넓혀가고 있는 스토리텔링이 사용자 친밀성에 미치는 영향과 사용자가 어떻게 엔터테인먼트 로봇과 친밀성을 형성해나가는지, 또 친밀성 요소들은 다양한 스토리텔링에서 서로 어떤 관련성을 가지고 친밀성 형성에 영향을 주는지를 연구 목적으로 삼고 있다. 이를 위해 스토리텔링과 관련된 대화가 가능한 지능형 엔터테인먼트 로봇에 대한 연구를 우선적으로 알아보았으며, 이를 토대로 엔터테인먼트 로봇 중 하나인 애완용 로봇, 아이보(AIBO)가 지니고 있는 스토리텔링을 통해 사용자와 로봇 간 친밀성 형성 및 스토리텔링을 바탕으로 한 친밀성 요소간의 관련성을 알아보았다. 참가자 15명의 설문조사를 바탕으로 실시한 상관관계분석을 통해 사용자와 아이보(AIBO)가 정서적, 오락적, 행동적, 상호공유성에서 인지적, 사회적 친밀성보다 상대적으로 강한 친밀성을 형성하고 있다는 것과 Alexis K. Walker 와 Linda Thompson(1983)을 기본으로 대인관계 측면에서 친밀성에 대해 접근한 이경희(1998), 김혜정(2001), 김미라(2001), 이금남(2001), 송상홍(2004)의 선행 연구를 참조로 분류된 6가지 차원의 친밀성 구성 요소들은 스토리텔링의 형식에 따라 서로 다른 관계성을 형성하고 있다는 것으로 나타났다. 본 논문은 아이보(AIBO)에 쓰이는 스토리텔링이 친밀성 형성에 어떤 영향을 미치는지 또 스토리텔링의 차원에 따라 다르게 반응을 보이는 친밀성 요소들이 서로 어떤 관련성을 맺고 있는지를 살펴본 것에 의의를 둘 수 있다. 스토리텔링에 따라 관련성 정도를 달리하는 친밀성 요소들의 관계를 바탕으로 친밀성 형성에 유용한 스토리텔링의 방향을 제시하는데 의의가 있다.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.184-197
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• 2016

This study investigates human-robot interactions in a museum environment. Unlike robots in movies, robots in real life do not have 'personality,' but instead have a specific purpose and function. This research is primarily about the assignment of specific and appropriate personalities to robots in different contexts. Methods by which a definite personality can be imparted on a robot are discussed in this research. Previous research has addressed attempts to assign a robot personality based on the task context. However, the discussion of robot personality is incomplete. Therefore, we assumed that a robot's character was dependent on the characteristics of its location of use. We planned a $2{\times}2$ comparison of various robot personalities, i.e., introverted or extroverted paired with either a traditional museum or a process-oriented experiential experience. Based on Schmitt's (1999) strategic experiential modules, experiential factors were derived for each set of conditions. To achieve the objectives of the research, theoretical and empirical research were conducted simultaneously. The software JMP 9 was used to verify the research model and hypotheses.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.179-185
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• 2014

In this paper, a quadruped walking pet robot for human-robot interaction, a robot-controller using a smart phone application program, and a home smart control system using sensor informations providing from the robot are described. The robot has 20 degree of freedom and consists of various sensors such as Kinect sensor, infrared sensor, 3 axis motion sensor, temperature/humidity sensor, gas sensor and graphic LCD module. We propose algorithms for the robot entertainment: walking algorithm of the robot, motion and voice recognition algorithm using Kinect sensor. emotional expression algorithm, smart phone application algorithm for a remote control of the robot, and home smart control algorithm for controlling home appliances. The experiments of this paper show that the proposed algorithms applied to the pet robot, smart phone, and computer are well operated.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.734-741
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• 2020

This article proposes a new pedestrian avoidance algorithm for social robots that coexist and communicate with humans and do not induce stress caused by invasion of psychological safety distance(Social Distance). To redefine the pedestrian model, pedestrians are clustered according to the pedestrian's gait characteristics(straightness, speed) and a social distance is defined for each pedestrian cluster. After modeling pedestrians(obstacles) with the social distances, integrated navigation algorithm is completed by applying the newly defined pedestrian model to commercial obstacle avoidance and path planning algorithms. To show the effectiveness of the proposed algorithm, two commercial obstacle avoidance & path planning algorithms(the Dynamic Window Approach (DWA) algorithm and the Timed Elastic Bands (TEB) algorithm) are used. Four cases were experimented in applying and non-applying the new pedestrian model, respectively. Simulation results show that the proposed algorithm can significantly reduce the stress index of pedestrians without loss of traveling time.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.565-570
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• 2013

The mirror neuron regions which are distributed in cortical area handled a functionality of intention recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper an automated intention recognition system is proposed by applying computational model of mirror neuron system to the human-robot interaction system. The computational model of mirror neuron system is designed by using dynamic neural networks which have model input which includes sequential feature vector set from the behaviors from the target object and actor and produce results as a form of motor data which can be used to perform the corresponding intentional action through the imitative learning and estimation procedures of the proposed computational model. The intention recognition framework is designed by a system which has a model input from KINECT sensor and has a model output by calculating the corresponding motor data within a virtual robot simulation environment on the basis of intention-related scenario with the limited experimental space and specified target object.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.361-366
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• 2020

The purpose of this study is to find out user preference on personification of the public service robot. Public service robot services in public places is increasing, which is expected to continue to increase. Proper anthropomorphism of robots has positive effect on user experience. On the other hand, when the level of likeness exceeds a certain point, it provokes strangeness and a sense of unease. Therefore, it is necessary to prepare standards for anthropomorphism required for public service robots. In order to find it, a survey and an in-depth interview were conducted. According to the analysis, people prefer verbal interaction with the robot, and the proper age for the voice is in their 20s and 30s. It is recommended that no biological signals appear on the robot and there is a need for personalized services. Through this research, it is expected that it will contribute to design of public service robots that enhance user experience.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.1-7
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• 2013

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.67-73
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• 2016

With the rapid development of mobile services and the prevalence of education robots, robots are being developed to become a part of our lives and they can be utilized to assist teachers in giving education or learning to students. This standard has been proposed to define the degree of autonomy for education robot. The autonomy is an ability to perform a given work based on current state and sensor value without human intervention. The degree of autonomy is a scale indicating the extent of autonomy and it is determined in between 1 and 10 by considering the level of work and human intervention. It has been adapted as per standard and education robots can be utilized in teaching the students autonomously. Education robots can be beneficial in education and it is expected to contribute in assisting the teacher's education.