• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.444-451
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• 2005

The analysis of human arm motion during steering maneuver is carried out for investigation of man-machine interface of driver and steering system Each arm is modeled as interconnection of upper arm, lower arm, and hand by rotational joints that can properly represents permissible joint motion, and both arms are connected to a steering wheel through spring and damper at the contact points. The joint motion law during steering motion is determined through the measurement of each arm movement, and subsequent inverse kinematic analysis. Combining the joint motion law and inverse dynamic analysis, joint stiffness of arm is estimated. Arm dynamic analysis model for steering maneuver is setup, and is validated through the comparison with experimentally measured data, which shows relatively good agreement. To demonstrate the usefulness of the arm model, it is applied to study the effect of steering column angle on the steering motion.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.59.4-59
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• 2001

In this paper, wireless interface of the motion between human and robot is implemented. The idea is that if a human who is equiped with device including accelerometer and rate-gyro sensor move his/her arm, then the robot follows human motion. The robot is designed as wheeled type mobile robot with two link arms. The robot´s basic movements such as forward, backward, left, right movement can be controlled from foot sensor which human steps on. Arm movements can be controlled by arm motion of human motion. In order to detect human motion, sensor data analysis from gyro and accelerometer has to be done. Data from sensors are transferred through wireless communication to activate the robot.

• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.68-79
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• 2014

Human model simulation has been widely used in various industrial areas such as ergonomic design, product evaluation and characteristic analysis of work-related musculoskeletal disorders. However, the process of building digital human models and capturing their behaviors requires many costly and time-consuming fabrication iterations. To overcome the limitations of this expensive and time-consuming process, many studies have recently presented a markerless motion capture approach that reconstructs the time-varying skeletal motions from optical devices. However, the drawback of the markerless motion capture approach is that the phenomenon of occlusion of motion data occurs in real-time human simulation. In this study, we propose a systematic method of discriminating missing or inaccurate motion data due to motion occlusion and interpolating a sequence of motion frames captured by a markerless depth camera.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.138-148
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• 2013

Purpose: Determining an appropriate path is a top priority in order for a robot to maneuver in a dynamically efficient way especially in a pick-and-place task. In a non-standardized work environment, current robot arm executes its motion based on the kinematic displacements of joint variables, though resulting motion is not dynamically optimal. In this research we suggest analyzing and applying motion patterns of the human arm as an alternative to perform near optimum motion trajectory for arbitrary pick-and-place tasks. Methods: Since the motion of a human arm is very complicated and diverse, it was simplified into two links: one from the shoulder to the elbow, and the other from the elbow to the hand. Motion patterns were then divided into horizontal and vertical components and further analyzed using kinematic and dynamic methods. The kinematic analysis was performed based on the D-H parameters and the dynamic analysis was carried out to calculate various parameters such as velocity, acceleration, torque, and energy using the Newton-Euler equation of motion and Lagrange's equation. In an attempt to assess the efficacy of the analyzed human motion pattern it was compared to the virtual motion pattern created by the joint interpolation method. Results: To demonstrate the efficacy of the human arm motion mechanical and dynamical analyses were performed, followed by the comparison with the virtual robot motion path that was created by the joint interpolation method. Consequently, the human arm was observed to be in motion while the elbow was bent. In return this contributed to the increase of the manipulability and decrease of gravity and torque being exerted on the elbow. In addition, the energy required for the motion decreased. Such phenomenon was more apparent under vertical motion than horizontal motion patterns, and in shorter paths than in longer ones. Thus, one can minimize the abrasion of joints by lowering the stress applied to the bones, muscles, and joints. From the perspectives of energy and durability, the robot arm will be able to utilize its motor most effectively by adopting the motion pattern of human arm. Conclusions: By applying the motion pattern of human arm to the robot arm motion, increase in efficiency and durability is expected, which will eventually produce robots capable of moving in an energy-efficient manner.

• Smart Media Journal
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• v.13 no.4
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• pp.16-22
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• 2024

Pitching is a major part of baseball, so much so that it can be said to be the beginning of baseball. Analysis of accurate pitching motions is very important in terms of performance improvement and injury prevention. When analyzing the correct pitching motion, the currently used motion capture method has several critical environmental drawbacks. In this paper, we propose analysis of pitching motion using the RGB-based Human Pose Estimation (HPE) model to replace motion capture, which has these shortcomings, and use motion capture data and HPE data to verify its reliability. The similarity of the two data was verified by comparing joint coordinates using the Dynamic Time Warping (DTW) algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.129-132
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• 2002

In this paper, we propose a 3-D animation system to track and analyze motion of the human object. The proposed system consists of two separate layers: motion analysis layer and 3-D model registration layer. Following the MPEG-4 SNHC standard, we generate object motion using body definition and animation parameters. In the implemented system, we acquire human motion data from a single camera and extract body definition parameters from arbitrary VRML human models.

• Journal of Mechanical Science and Technology
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• v.19 no.10
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• pp.1919-1931
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• 2005

Human joint motion can be kinematically described in three planes, typically the frontal, sagittal, and transverse, and related to experimentally measured data. The selection of reference systems is a prerequisite for accurate kinematic analysis and resulting development of the equations of motion. Moreover, the development of analysis techniques for the minimization of errors, due to skin movement or body deformation, during experiments involving human locomotion is a critically important step, without which accurate results in this type of experiment are an impossibility. The traditional kinematic analysis method is the Angular-based method (ABM), which utilizes the Euler angle or the Bryant angle. However, this analysis method tends to increase cumulative errors due to skin movement. Therefore, the objective of this study was to propose a new kinematic analysis method, Position-based method (PBM), which directly applies position displacement data to represent locomotion. The PBM presented here was designed to minimize cumulative errors via considerations of angle changes and translational motion between markers occurring due to skin movements. In order to verify the efficacy and accuracy of the developed PBM, the mean value of joint dislocation at the knee during one gait cycle and the pattern of three dimensional translation motion of the tibiofemoral joint at the knee, in both flexion and extension, were accessed via ABM and via new method, PBM, with a Local Reference system (LRS) and Segmental Reference system (SRS), and then the data were compared between the two techniques. Our results indicate that the proposed PBM resulted in improved accuracy in terms of motion analysis, as compared to ABM, with the LRS and SRS.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.8 no.2
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• pp.19-26
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• 1989

This study, as a part of integrated human-robot ergonomics, improves the robot motion control on the robot task in the TOES/WCS whose purpose is improving the teaching task constructed in the previous study. First, the updated combined fuzzy process using a new membership function with Weber's law is constructed for the purpose of coordinate reading of the end points in the macro motion control. Second, an algorithm using the geometric analysis is desinged in order to calculate position values and posture values of the robot joints. Third, the MGSLM method is designed to remove unnecessary the robot motion control caused by the GSLM method in the micro motion control. Consequently, proposed methods in this study lessen burdcn of a human of an improvement of the robot motion control and reduce the teaching time of a human operator and inaccuracy of the teaching task, which contribute to the integrated human-robot ergonomics.