• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.1-10
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• 2010

This present paper's goal is to show a virtual human agent's movement-data loci based on realistically limited perception data is human-like. To determine human-likeness of the movement-data loci, we consider interactions between two parameters: Realistically Limited Perception (RLP) data and Incremental Movement-Path data Generation (IMPG). That is to consider how the former (i.e., RLP), one of the simulated parameters of human thought or its elements dictates the latter (i.e., IMPG), one of the simulated parameters of human movement behavior. Mapping DB is a prerequisite for navigation in an agent system because it functions as an interface between perception and movement behavior. Although Hill et al. studied mapping DB methodology based on RLP, their research dealt only with a rendering camera's view point data. The agent system in this present paper was integrated with the Hill's mapping DB module and then the two parameters' interaction was considered on a military reconnaissance mission with unexpected enemy emergence. Movement loci that were generated by the agent and subjects were compared with each other. The agent system in this present research verifies that it can be a functional test bed for producing human-like movement-data loci although the human-likeness of agent is the result of a pilot test, determined by two parameters (RLP and IMPG) and only 30 subjects.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.374-379
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• 2002

In this study, we developed a new human arm movement detection system using electrical bio-impedance method with several skin-electrodes. The correlation coefficients of the joint angle and the impedance change from human arm movement was obtained using a goniometer and impedance measurement system developed in this study. The correlation coefficients of the wrist and the elbow movements were 0.94 and -0.99, respectively. This system was applied to control a robotic arm by converting the measured impedance to joint angle to confirm the validity of the proposed system. In conclusion, we confirmed that this system can control the robotic arm according to arm movement without any limitation of movement. This system showed possibility that upper arm movement could be easily measured by impedance measurement system with a few skin-electrodes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.121-130
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• 2012

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.1038-1046
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• 2008

This paper presents a framework to generate human-like movements of a humanoid in real time using the movement primitive database of a human. The framework consists of two processes: 1) the offline motion imitation learning based on an Evolutionary Algorithm and 2) the online motion generation of a humanoid using the database updated bγ the motion imitation teaming. For the offline process, the initial database contains the kinetic characteristics of a human, since it is full of human's captured motions. The database then develops through the proposed framework of motion teaming based on an Evolutionary Algorithm, having the kinetic characteristics of a humanoid in aspect of minimal torque or joint jerk. The humanoid generates human-like movements far a given purpose in real time by linearly interpolating the primitive motions in the developed database. The movement of catching a ball was examined in simulation.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.164-170
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• 2004

The skin movement artifacts are referred to as the relative motion of skin with respect to the motion of underlying bones. This is of great importance in joint biomechanics or internal kinematics of human body. This paper describes a novel experiment that measures the skin movement of a hand based on MR(magnetic resonance) images in conjunction with surface modeling techniques. The proposed approach consists of 3 phases: (1) MR scanning of a hand with surface makers, (2) 3D reconstruction from the MR images, and (3) registration of the 3D models. The MR images of the hand are captured by 3 different postures. And the surface makers which are attached to the skin are employed to trace the skin motion. After reconstruction of 3D models from the scanned MR images, the global registration is applied to the 3D models based on the particular bone shape of different postures. The results of registration are then used to trace the skin movement by measuring the positions of the surface markers.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.649-663
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• 2004

While a great number of studies on human motor control have provided a wide variety of viewpoints concerning the strategy of the central nervous system (CNS) in controlling limb movement, none were able to reveal the exact methods how the movement command from CNS is mapped onto the neuromuscular activity. As a preliminary study of human-machine interface design, the characteristics of human leg motion and its underlying motor control scheme are studied through experiments and simulations in this paper. The findings in this study suggest a simple open-loop motor control scheme in leg motion. As a possible candidate, an equilibrium point control model appears consistent in recreating the experimental data in numerical simulations. Based on the general leg motion analysis, the braking motion by the driver's leg is modeled.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.857-860
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• 2014

This paper proposes a human movement detection system by a thermopile array sensor. In the system, the sensor is attached to the ceiling and it acquires spatial temperatures, which is called thermal distribution. The system obtains $4{\times}4$ pixels thermal distributions from the sensor. The distributions are analyzed to extract human movement. As the experimental result, the proposed system successfully detected human movements.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.24-29
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• 2014

This paper proposes a kinect-based human motion recognition model for the 3D contents control after tracking the human body gesture through the camera in the infrared kinect project. The proposed human motion model in this paper computes the distance variation of the body movement from shoulder to right and left hand, wrist, arm, and elbow. The human motion model is classified into the movement directions such as the left movement, right movement, up, down, enlargement, downsizing. and selection. The proposed kinect-based human motion recognition model is very natural and low cost compared to other contact type gesture recognition technologies and device based gesture technologies with the expensive hardware system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.561-568
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• 1994

A man model can be used as an effective tool to design ergonomically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movement was known to be a difficult problem. To solve this redundancy problem, a psychophysical cost function was suggested in this study which defines a cost value for each joint movement angle. The psychophysical cost function developed integrates the psychophysical discomfort of joints and the joint range availability concept which has been used for redundant arm manipulation in robotics to predict the arm reach posture. To properly predict an arm reach posture, an arm reach posture prediction model was then developed in which a posture configuration that provides the minimum total cost is chosen. The predictivity of the psychophysical cost function was compared with that of the biomechanical cost function which is based on the minimization of joint torque. Here, the human body is regarded as a two-dimensional multi-link system which consists of four links ; trunk, upper arm, lower arm and hand. Real reach postures were photographed from the subjects and were compared to the postures predicted by the model. Results showed that the postures predicted by the psychophysical cost function closely simulated human reach postures and the predictivity was more accurate than that by the biomechanical cost function.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.87-94
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• 2020

This paper introduces a method to control an industrial robot arm to imitate the movement of the human arm and hand using electromyography (EMG) signals. The proposed method is implemented on the UR3 robot that is a popular industrial robot and a MYO armband that measure the EMG signals generated by human muscles. The communications for the UR3 robot and the MYO armband are integrated in the robot operating system (ROS) that is a middle-ware to develop robot systems easily. The movement of the human arm and hand is detected by the MYO armband, which is utilized to recognize and to estimate the speed of the movement of the operator's arm and the motion of the operator's hand. The proposed system can be easily used when human's detailed movement is required in the environment where human can't work. An experiments have been conducted to verify the performance of the proposed method using the teleoperation of the UR3 robot.