• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.264-268
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• 2005

This paper describes user context awareness system, which is one of the most essential technologies in various application services of ubiquitous computing. The proposed system used two-axial accelerometer, embedded in $SenseWear^{(R)}$ PRO2 Armband (BodyMedia). It was worn on the right upper arm of the experiment subjects. Using this data, PC-based fuzzy inference system was realized to distinguish human motion states, such as, tying, sitting, walking and running. The recognition rates of human motion states were 100 %, 98.64 %, 99.27 % and 100 % respectively for tying, sitting, walking and running.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.75-83
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• 2007

A model based approach of tracking the limbs of a walking human subject is proposed in this paper. The tracking process begins by building a data base composed of conditional probabilities of motions between the limbs of a walking subject. With a suitable amount of video footage from various human subjects included in the database, a probabilistic model characterizing the relationships between motions of limbs is developed. The motion tracking of a test subject begins with identifying and tracking limbs from the surveillance video image using the edge and silhouette detection methods. When occlusion occurs in any of the limbs being tracked, the approach uses the probabilistic motion model in conjunction with the minimum cost based edge and silhouette tracking model to determine the motion of the limb occluded in the image. The method has shown promising results of tracking occluded limbs in the validation tests.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.9
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• pp.905-912
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• 2008

Notion sickness is well known to be caused by long time exposure to the very low frequency motion in the multiple axes of human body Since the vestibular system for the perception of low frequency motion is located in the head, accurate measurement of 6 degree of freedom head motion is of great importance. In this study, the measurement system consisting of a safety helmet and 9 translational accelerometers was constructed for the estimation of 3 translational and 3 rotational motions of human head. Since estimation errors of 3 rotational components can be significantly magnified even by small offset of the sensitivity axis from the geometric center of an accelerometer, accurate measurement of sensitivity axis must be preceded. The method for accurate estimation of the offset was proposed, and the effect of offset on the estimation of angular acceleration was investigated.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.140-150
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• 1990

Further developments on a dynamic biomechanical model are presented to assess musculoskeletal stresses and human responses. The model being developed is an extension of the Articulated Total Body (ATB) Model, originally developed by Calsapan Corp. for the study of human dynamics during automobile crashes, later adopted to the U.S.Air Force to simulate the reactions of aircrew personnel to such forces typically encountered in various phases of flight operations. Further refinements were introduced by Freivalds and Kaleps(1984) to account for a human neuromusculature. In this study, modelling of active neuromusculature was described and simulations of whole-body human motion were performed using the ATB Model. It indicated the potential of using a muscularized biomechanical model coupled with CAD capabilities to simulate human responses in a variety of industrial settings as well. This will serve as a basis of incorporating computer aided design methods into a muscularized biomechanical models.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.913-918
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• 2007

Human motion analysis is an important research subject in human-robot interaction (HRI). However, before analyzing the human motion, silhouette of human body should be extracted from sequential images obtained by CCD camera. The intelligent robot system requires more robust silhouette extraction method because it has internal vibration and low resolution. In this paper, we discuss the hybrid silhouette extraction method for detecting and tracking the human motion. The proposed method is to combine and optimize the temporal and spatial gradient information. Also, we propose some compensation methods so as not to miss silhouette information due to poor images. Finally, we have shown the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.446-453
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• 1999

An athlete motion during weightlifting is analyzed based on robotic manipulability, which shows dexterities by changing the position and orientation of the end-effector of robot manipulators arbitrary or along a specified direction. The athlete body is modeled as a highly redundant robot manipulator. The motion of weightlifting is analyzed based on the selected model with a power manipulability. Power manipulability and its geometric characteristics are derived by combining kinematic manipulability and dynamic manipulability. Also, manipulability-based optimal trajectory of weightlifter for given body structure of weightlifter derived through genetic algorithm.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.217-222
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• 1995

We propose some applications of image processing techniques to extract quantitative measurements by using a camera system developed in Korea university and Catholic Medical School. From now on the system will be called as KCMOTION. The purpose of this study is to provide basic kinematic and kinetic data for the analysis of human movements and to find the clinical usefulness and reliability of the proposed motion analysis system. Two tests, sit-to-stand (STS) movements and pendulum test, are conducted by the system. The aims of the tests are to identify variability and reliability of KCMOTION to give some quantitative comparisons to the other systems. The result of STS movement are compared to the LOCUS IIID motion analyzer by the ratio of maximum flexion movement per body weight to the actual maximum flexion extension torque per body weight. That result in 29 % and 33 % for hip and knee joint, respectively in KCMOTION and 27 % and 30 % in LOCUS IIID System. The results of the pendulum movements are compared to that of using Cybex and Electrogoniometer with relaxation index, amplitude ratio, swing number and swing time. The results of relaxation index and amplitude ratio of the KCMOTION are between those of the Cybex and Electrogoniometer. We also observed that the KCMOTION detect more natural movement, from the results of swing number and time.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.176-183
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• 2009

This paper proposes command signal generating method for a wearable robot using the force as the input signal. The basic concept of this system pursues the combination of the natural and sophisticated intelligence of human with the powerful motion capability of the robot. We define a task for the command signal generation to operate with the human body simultaneously, paying attention to comfort and ease of wear. In this study, we suggest a basic exoskeleton experimental system to evaluate a HRI(Human Robot Interface), selecting interfaces of arm braces on both wrists and a weight harness on the torso to connect the robot and human. We develop the HRI to provide a command for the robot motion. It connects between the human and the robot with the multi-axis load-cell, and it measures the relative force between the human and the robot. The control system calculates the trajectory of end-effector using this force signal. In this paper, we verify the performance of proposed system through the motion of elbow E/F(Extension/Flexion), the shoulder E/F and the shoulder Ab/Ad (Abduction/Adduction).

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.377-386
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• 2015

Joint force/torque estimation by inverse dynamics is a traditional tool in biomechanical studies. Conventionally for this, kinematic data of human body is obtained by motion capture cameras, of which the bulkiness and occlusion problem make it hard to capture a broad range of movement. As an alternative, inertial motion sensing using cheap and small inertial sensors has been studied recently. In this research, the performance of inertial motion sensing especially to calculate inverse dynamics is studied. Kinematic data from inertial motion sensors is used to calculate ground reaction force (GRF), which is compared to the force plate readings (ground truth) and additionally to the estimation result from optical method. The GRF estimation result showed high correlation and low normalized RMSE(R=0.93, normalized RMSE<0.02 of body weight), which performed even better than conventional optical method. This result guarantees enough accuracy of inertial motion sensing to be used in inverse dynamics analysis.