• Journal of Digital Contents Society
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• v.13 no.2
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• pp.255-262
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• 2012

Human motion tracking algorithm is receiving attention from many research areas, such as human computer interaction, video conference, surveillance analysis, and game or entertainment applications. Over the last decade, various tracking technologies for each application have been demonstrated and refined among them such of real time computer vision and image processing, advanced man-machine interface, and so on. In this paper, we introduce cost-effective and real-time human motion tracking algorithms based on depth image 3D point matching with a given superellipsoid body representation. The body representative model is made by using parametric volume modeling method based on superellipsoid and consists of 18 articulated joints. For more accurate estimation, we exploit initial inverse kinematic solution with classified body parts' information, and then, the initial pose is modified to more accurate pose by using 3D point matching algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• ETRI Journal
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• v.45 no.3
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• pp.543-550
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• 2023

Electrostatic discharge (ESD) testing for human body model tests is an essential part of the reliability evaluation of electronic/electrical devices and components. However, global environmental concerns have called for the need to replace the mercury-wetted relay switches, which have been used in ESD testers. Therefore, herein, we propose an ESD tester using metal oxide semiconductor-controlled thyristor (MCT) devices with a significantly higher rising rate of anode current (di/dt) characteristics. These MCTs, which have a breakdown voltage beyond 3000 V, were developed through an in-house foundry. As a replacement for the existing mercury relays, the proposed ESD tester with the developed MCT satisfies all the requirements stipulated in the JS-001 standard for conditions at or below 2000 V. Moreover, unlike traditional relays, the proposed ESD tester does not generate resonance; therefore, no additional circuitry is required for resonant removal. To the best of our knowledge, the proposed ESD tester is the first study to meet the JS-001 specification by applying a new switch instead of an existing mercury-wetted relay.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.1
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• pp.83-90
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• 2008

Taekwondo is a martial art form and sport that uses the hands and foot for attack and defense. Taekwondo basic motion is composed of the breaking, competition and poomsea motion. In the side kick among the competition motion, the impact force is larger than other kinds of kicks. The side kick with the front foot can be made in two steps. In the first step, the front foot is stretched forward from back stance free-fighting position. For the second step, the rear foot is followed simultaneously. Then, the kick is executed while entire body weight rests on the rear foot. In this paper, impact analysis of the human model for hitting posture is carried out. The ADAMS/LifeMOD is used in hitting modeling and simulation. The simulation model creates the human model to hit the opponent. As the results, the dynamic analysis of human muscle were presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.329-331
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• 2021

Recently, as various cases of using deep learning in the health-care field are increasing, functions such as electrocardiogram examination and body composition analysis through wearable device can be provided to provide rational decision-making and a process tailored to the individual. In order to utilize deep learning, it it most important to secure refined data, and this data is being made through human intervention or unsupervised learning. In this paper, we propose a model that conducts unsupervised learning by clusters according to gender and age using human body data such as chest and waist circumferences, which are easy to measure, and classifies them with CNN. For data, the 7th human body data provided by Korean Agency for Technology and Standards was used. Through this, it it thought that it can be applied to various application cases such as personalized body shape management service and obesity analysis.

• Journal of the Ergonomics Society of Korea
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• v.21 no.2
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• pp.13-23
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• 2002

Human dummies are essential tools in the development of such products as vehicle have been actively used not only in reach and view field tests. but also in impact perception evaluations. This study attempted to obtain geometric and dynamic model body segments from Korean anthropometric data. The investigation focused on the de both human and dummy for the geometric and inertial properties. The dynamic modeli being suggested is based on rigid body dynamics using fifteen individual body segments by joins. The segments are connected at the locations representing the physical joint body so that each segment has its mass and moment of inertia. For visual three-dimensional graphic was used for easier implementation of the dumn applications. For applications, proposed Korean dummies Were used in dynamic crash and driver's view and reach test modules were developed in virtual environment.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.182-189
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• 2005

The human knee joint is the intermediate joint of the lower limb that is the largest and most complex joint in the body. Understanding of joint-articulating surface motion is essential for the joint wear, stability, mobility, degeneration, determination of proper diagnosis and so on. However, many studies analyzed the passive motion of the lower limb because of the skin marker artefact and some studies described medial and lateral condyle of a femur as a simple sphere due to the complexity of geometry. Thus, in this paper, we constructed a three-dimensional geometric model of the human knee from the geometry of its anatomical structures using non-uniform B-spline surface fitting as a study for the kinematic analysis of more realistic human knee model. In addition, we developed and verified 6-DOF contact model of the human knee joint using $C^2$ continuous surface of the inferior region of a femur, considering the relative motion of shank to thigh during locomotion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.903-906
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• 1995

In this paper,human body models have been manufactured by the use of rapid prototyping techniques, which are to be used for surgery planning in clinical practice. In this manufactacturing process, CT or MRI data of human bodies are prepared and the images are processed to obtain sectional contours. With these contours, three-dimensional surface triangulated models are constructed, which finally transformed to STL file for rapid prototyping. For this purpose, total service system for manufacturing of human body models is constructed by employing commercial softwares, and the related problems and process parameters are investigated

• ETRI Journal
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• v.39 no.2
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• pp.181-190
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• 2017

The movements of the human body are difficult to capture owing to the complexity of the three-dimensional skeleton model and occlusion problems. In this paper, we propose a motion capture system that tracks dynamic human motions in real time. Without using external markers, the proposed system adopts multiple depth sensors (Microsoft Kinect) to overcome the occlusion and body rotation problems. To combine the joint data retrieved from the multiple sensors, our calibration process samples a point cloud from depth images and unifies the coordinate systems in point clouds into a single coordinate system via the iterative closest point method. Using noisy skeletal data from sensors, a posture reconstruction method is introduced to estimate the optimal joint positions for consistent motion generation. Based on the high tracking accuracy of the proposed system, we demonstrate that our system is applicable to various motion-based training programs in dance and Taekwondo.