• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.55-61
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• 2013

For years, many studies have mainly been investigated in a complicated human motion analysis. Recently, many motion analysis equipments have been studied and developed. Therefore, the more complex human movement analyses are possible, we have enabled us to perform more and more complicated human movement analyses. A Three-dimensional(3D) motion analysis on of the several methods is a useful tool for analyzing the human motion analysis. The purpose of this study was to develop the 3D human motion analysis using a kalman filter algorithm and a gyro sensor. The algorithm and sensor were used to human motion analysis with high-speed motion capture. In this study, the developed system will be adapted to facilitate golf swing analysis. Using the developed system, golfers and coaches who do not have advanced biomechanical knowledge can easily be used to their golf swing analysis. Future study is necessary for more practical and efficient area such as other sports industries, 3D game industries, rehabilitation training, etc..

• ETRI Journal
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• v.33 no.2
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• pp.259-266
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• 2011

We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: I) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angles and body points, and iii) analysis of motion parameters and feature extraction for classifying human gait. A sequential set of 2D stick figures is used to represent the human gait motion, and the features based on motion parameters are determined from the sequence of extracted gait figures. Then, a k-nearest neighbor classifier is used to classify the gait patterns. In experiments, this provides an alternative estimate of biomechanical parameters on a large population of subjects, suggesting that the estimate of variance by marker-based techniques appeared generous. This is a very effective and well-defined representation method for analyzing the gait motion. As such, the markerless approach confirms uniqueness of the gait as earlier studies and encourages further development along these lines.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.178-183
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• 2008

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4624-4644
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• 2019

With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important. Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques. However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data. They cannot use the data information fully. In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem. First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly. Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation. The experiments demonstrate that our framework has an excellent performance.

• 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.11 no.6
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• pp.543-549
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• 2005

This paper deals with modeling and analysis fer the landing motion of a human-body model. First, the dynamic model of a floating human body is derived. The external impulse exerted on the ground as well as the internal impulse experienced at the joints of the human body model is analyzed. Second, a motion planning algorithm exploiting the kinematic redundancy is suggested to ensure stability in terms of ZMP stability condition during a series of landing phases. Four phases of landing motion are investigated. In simulation, the external and internal impulses experienced at the human joints and the ZMP history resulting from the motion planning are analyzed for two different configurations. h desired landing posture is suggested by comparison of the simulation results.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.11
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• pp.116-125
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• 2007

Human muscle skeletal model was developed for biomechanical study. The human model was consists with 19 bone-skeleton and 122 muscles. Muscle number of upper limb, trunk and lower limb part are 28, 60, 34 respectively. Bone was modeled with 3D beam element and muscle was modeled with spar element. For upper limb muscle modelling, rectus abdominis, trapezius, deltoideus, biceps brachii, triceps brachii muscle and other main muscles were considered. Lower limb muscle was modeled with gastrocenemius, gluteus maximus, gluteus medius and related muscles. The biomechanical stress and strain analysis of human was conducted by proposed finite element analysis model under Kumdo head hitting motion. In this study structural analysis has been performed in order to investigate the human body impact by Kumdo head hitting motion. As the results, the analytical displacement, stress and strain of human body are presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1095-1102
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• 2014

In this paper, We proposed the approach of GPU and motion vector to analysis the Human activity in real-time surveillance system. The most important part, that is detect blob(human) in the foreground. We use to detect Adaptive Gaussian Mixture, Weighted subtraction image for salient motion and motion vector. And then, We use motion vector for human activity analysis. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}. We created approximately 300 conditions for the simulation. As a result, We showed a high success rate about 86~98%. The results also showed that the high resolution experiment by the proposed GPU-based method was over 10 times faster than the cpu-based method.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.135-140
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• 2001

In the last years there have been some severe accidents with passenger vessels. So, International Maritime Organization(IMO) has recognized that computer stimulation of the evacuation may be required for passenger vessels. Human elements is a key issues of escape analysis on shipboard. There are technical requirements to simulate of escape analysis for human elements. Technical requirements include model of ship structure, evacuation algorithm, human behaviour analysis and influence of ship listing/motion. This paper provides the key issues and technologies of simulation for human escape on shipboard.

• Physical Therapy Korea
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• v.29 no.2
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• pp.87-93
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• 2022

To solve the pathological problems of the musculoskeletal system based on evidence, a sophisticated analysis of human motion is required. Traditional optical motion capture systems with high validity and reliability have been utilized in clinical practice for a long time. However, expensive equipment and professional technicians are required to construct optical motion capture systems, hence they are used at a limited capacity in clinical settings despite their advantages. The development of information technology has overcome the existing limit and paved the way for constructing a motion capture system that can be operated at a low cost. Recently, with the development of computer vision-based technology and optical markerless tracking technology, webcam-based 3D human motion analysis has become possible, in which the intuitive interface increases the user-friendliness to non-specialists. In addition, unlike conventional optical motion capture, with this approach, it is possible to analyze motions of multiple people at simultaneously. In a non-optical motion capture system, an inertial measurement unit is typically used, which is not significantly different from a conventional optical motion capture system in terms of its validity and reliability. With the development of markerless technology and advent of non-optical motion capture systems, it is a great advantage that human motion analysis is no longer limited to laboratories.