• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.212-220
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• 2013

Recently, there has been tremendous increase in human-computer/machine interaction system, where the goal is to provide with an appropriate service to the user at the right time with minimal human inputs for human augmented cognition system. To develop an efficient human augmented cognition system based on human computer/machine interaction, it is important to interpret the user's implicit intention, which is vague, in addition to the explicit intention. According to cognitive visual-motor theory, human eye movements and pupillary responses are rich sources of information about human intention and behavior. In this paper, we propose a novel approach for the identification of human implicit visual search intention based on eye movement pattern and pupillary analysis such as pupil size, gradient of pupil size variation, fixation length/count for the area of interest. The proposed model identifies the human's implicit intention into three types such as navigational intent generation, informational intent generation, and informational intent disappearance. Navigational intent refers to the search to find something interesting in an input scene with no specific instructions, while informational intent refers to the search to find a particular target object at a specific location in the input scene. In the present study, based on the human eye movement pattern and pupillary analysis, we used a hierarchical support vector machine which can detect the transitions between the different implicit intents - navigational intent generation to informational intent generation and informational intent disappearance.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1960-1963
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• 2003

For applications such as video surveillance and human computer interface, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined to the algorithm: motion, skin color, global appearance and facial pattern detection. The ICA (Independent Component Analysis)-SVM (Support Vector Machine based pattern detection is performed on the candidate region extracted by motion, color and global appearance information. Simultaneous execution of detection and short-term tracking also increases the rate and accuracy of detection. Experimental results show that our detection rate is 91％ with very few false alarms running at about 4 frames per second for 640 by 480 pixel images on a Pentium IV 1㎓.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.7-12
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• 2008

In human and robot interaction, the role of emotion becomes more important Therefore, robots need the emotion expression mechanism similar to human. In this paper, we suggest a new emotion expression system based on the psychological studies and it consists of five affective elements, i.e., the emotion, the mood, the personality, the tendency, and the machine rhythm. Each element has somewhat peculiar influence on the emotion expression pattern change according to their characteristics. As a result, although robots were exposed to the same external stimuli, each robot can show a different emotion expression pattern. The proposed system may contribute to make a rather natural and human-friendly human-robot interaction and to promote more intimate relationships between people and robots.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.28-33
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• 2012

The visual sensing of human hands plays an important part in many man-machine interaction/interface systems. Most existing visionbased hand detection techniques depend on the color cues of human skin. The RGB color image from a vision sensor is often transformed to another color space as a preprocessing of hand detection because the color space transformation is assumed to increase the detection accuracy. However, the actual effect of color space transformation has not been well investigated in literature. This paper discusses a comparative evaluation of the pixel classification performance of hand skin detection in four widely used color spaces; RGB, YIQ, HSV, and normalized rgb. The experimental results indicate that using the normalized red-green color values is the most reliable under different backgrounds, lighting conditions, individuals, and hand postures. The nonlinear classification of pixel colors by the use of a multilayer neural network is also proposed to improve the detection accuracy.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.129-138
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• 2022

With the increasing reliance of computing systems in our everyday life, there is always a constant need to improve the ways users can interact with such systems in a more natural, effective, and convenient way. In the initial computing revolution, the interaction between the humans and machines have been limited. The machines were not necessarily meant to be intelligent. This begged for the need to develop systems that could automatically identify and interpret our actions. Automatic gesture recognition is one of the popular methods users can control systems with their gestures. This includes various kinds of tracking including the whole body, hands, head, face, etc. We also touch upon a different line of work including Brain-Computer Interface (BCI), Electromyography (EMG) as potential additions to the gesture recognition regime. In this work, we present an overview of several applications of automated gesture recognition systems and a brief look at the popular methods employed.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3460-3463
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• 2021

Present computing power and enhanced technology is progressing at a dramatic rate. These systems can unravel complex issues, assess and control processes, learn, and-in many cases-fully automate production. There is no doubt that technological advancement is improving many aspects of life, changing the landscape of virtually all industries and enhancing production beyond what was thought possible. However, the human is still a part of these systems. Consequently, as the advancement of systems transpires, the role of humans within those systems will unavoidably continue to adapt as well. Due to the human tendency for error, this technological advancement should compel a persistent emphasis on human error reduction as part of maximizing system efficiency and safety-especially in the context of the nuclear industry. Within this context, as new systems are designed and the role of the human is transformed, human error should be targeted for a significant decrease relative to predecessor systems and an equivalent increase in system stability and safety. This article contends that optimizing the roles of humans and machines in the design and implementation of new types of automation in nuclear facility systems should involve human error reduction without ignoring the essential importance of human interaction within those systems.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.06a
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• pp.55-63
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• 1995

Press accidents are one of the most popular industrial accidents. Two factors are considered to lead to press accidents, one is human, the other press machine itself. Accident itself is analyzed and concluded to be due to incomplete interaction between press and human. A press design considering safety as a methodology is introduced to reduce the industrial accidents by press machine, that is micro-inching locking system.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.663-669
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• 2011

This paper examines methods to estimate torque and angle in shoulder flexion from surface electromyography(sEMG) signals for intuitive and delicate control of robotic assistance device. Five muscles on the upper arm, three for shoulder flexion and two for shoulder extension, were used to offer favorable sEMG recording conditions in the estimation. The methods tested were the mean absolute value (MAV) with linear regression and the artificial neural network (ANN) method. An optimal condition was sought by varying combination of muscles used and the parameters in each method. The estimation performance was evaluated using the correlation values and normalized root mean square error values. In addition, we discussed their possible use as an estimation of motion intent of a user or as a command input in a physical human-machine interaction system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2676-2681
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• 2003

The Haptic Exploration Laboratory at The Johns Hopkins University is currently exploring many problems related to haptics (force and tactile information) in human-machine systems. We divide our work into two main areas: virtual environments and robot-assisted manipulation systems. Our interest in virtual environments focuses on reality-based modeling, in which measurements of the static and dynamic properties of actual objects are taken in order to produce realistic virtual environments. Thus, we must develop methods for acquiring data from real objects and populating pre-defined models. We also seek to create systems that can provide active manipulation assistance to the operator through haptic, visual, and audio cues. These systems may be teleoperated systems, which allow human users to operate in environments that would normally be inaccessible due to hazards, distance, or scale. Alternatively, cooperative manipulation systems allow a user and a robot to share a tool, allowing the user to guide or override the robot directly if necessary. Haptics in human-machine systems can have many applications, such as undersea and space operations, training for pilots and surgeons, and manufacturing. We focus much of our work on medical applications.