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Hyong-Euk Lee

Bio: Hyong-Euk Lee is an academic researcher from KAIST. The author has contributed to research in topics: Robot & Human–robot interaction. The author has an hindex of 7, co-authored 16 publications receiving 269 citations.

Papers
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Proceedings ArticleDOI
Jeong-Su Han1, Z. Zenn Bien1, Dae-Jin Kim1, Hyong-Euk Lee1, Jong-Sung Kim 
17 Sep 2003
TL;DR: This paper classified the pre-defined motions such as rest case, forward movement, left movement, and right movement by fuzzy min-max neural networks (FMMNN) and shows the feasibility of EMG as an input interface for powered wheelchair.
Abstract: The objective of this paper is to develop a powered wheelchair controller based on EMG for users with high-level spinal cord injury. EMG is very naturally measured when the user indicating a certain direction and the force information which will be used for the speed of wheelchair is easily extracted from EMG. Furthermore, the emergency situation based on EMG will be checked relatively ease. We classified the pre-defined motions such as rest case, forward movement, left movement, and right movement by fuzzy min-max neural networks (FMMNN). This classification results and evaluation results with real users shows the feasibility of EMG as an input interface for powered wheelchair.

97 citations

Journal ArticleDOI
Z. Zenn Bien1, Hyong-Euk Lee1
TL;DR: It is shown that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth.
Abstract: HRI (Human-Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.

53 citations

Journal ArticleDOI
TL;DR: This work presents a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustered process that requires less human intervention and less prior knowledge than other state of the art methods.
Abstract: To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.

48 citations

Journal ArticleDOI
TL;DR: A probabilistic fuzzy rule-based life-l ong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations is discussed.
Abstract: The smart house under consideration is a service-integrated complex system to assist older persons and/or people with disabilities. The primary goal of the system is to achieve independent living by various robotic devices and systems. Such a system is treated as a human-in-the loop system in which humanrobot interaction takes place intensely and frequently. Ba sed on our experiences of having designed and implemented a smart house environment, called Intelligent Sweet Home (ISH), we present a framework of realizing human-friendly HRI (human-robot interaction) module with various effective techniques of computational intelligence. More specifically, we partiti on the robotic tasks of HRI module into three groups in consideration of the level of specificity, fuzzine ss or uncertainty of the context of the system, and present effective interaction method for each case. We fi rst show a task planning algorithm and its architecture to deal with well-structured tasks autonomously by a simplified set of commands of the user instead of inconvenient manual operations. To provide with capability of interacting in a human-friendly way in a fuzzy context, it is proposed that the robot should make use of human bio-signals as input of the HRI module as shown in a hand gesture recognition system, called a soft remote control system. Finally we discuss a probabilistic fuzzy rule-based life-l ong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations.

32 citations

Proceedings ArticleDOI
Heon-Hui Kim1, Hyong-Euk Lee1, Yong-Hwi Kim1, Kwang-Hyun Park1, Zeungnam Bien1 
26 Aug 2007
TL;DR: An automatic gesture generation methodology for conversational interaction of service robots is presented toward more human-friendly human-robot interaction and the effectiveness of the proposed system with the simulated motions of robot is discussed.
Abstract: Recently, in service robotics area, increasing attention is being paid on interaction capability of robot for human-being as well as its task performing capability. In this paper, in particular, an automatic gesture generation methodology for conversational interaction of service robots is presented toward more human-friendly human-robot interaction. From the survey on the results in the psychology field, we first categorized the target gestures into the three types of gestures, which are basic, supplementary/emphasizing, and finishing/interconnective gestures with their corresponding unit gesture components. Then, a set of mapping rules have been extracted for gesture generation based on observing human behavioral patterns during conversation, by means of morpheme decomposition and analysis. From the given text input in Korean, the proposed system tries to generate robotic gestures, which consist of the head and the arm motions, by gesture selection and motion scheduling schemes. Finally, we discuss on the effectiveness of the proposed system with the simulated motions of robot as an initial attempt to apply in a practical system.

14 citations


Cited by
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Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

1,814 citations

Book
25 Jan 2008
TL;DR: The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future.
Abstract: Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

1,602 citations

Journal ArticleDOI
TL;DR: This paper reviews recent research and development in pattern recognition- and non-pattern recognition-based myoelectric control, and presents state-of-the-art achievements in terms of their type, structure, and potential application.

1,111 citations

Journal ArticleDOI
TL;DR: McNeill as discussed by the authors discusses what Gestures reveal about Thought in Hand and Mind: What Gestures Reveal about Thought. Chicago and London: University of Chicago Press, 1992. 416 pp.
Abstract: Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

988 citations

Journal ArticleDOI
TL;DR: This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics, often designed as components of the larger smart home environment.

935 citations