• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.172-178
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• 2023

With the expansion of metaverse content and hardware platforms, various interactions in the virtual world have been built, raising expectations for an increase in immersion which is a major element of the metaverse. However, among hardware platforms that increase virtual immersion elements, the typical HMD platform can be a barrier to new user inflows due to its high cost. Thus, this paper focused on improving virtual-to-real interactions by extracting motion data using relatively inexpensive webcam equipment in PC environments, utilizing Unity game engines, Photon unity network, multi-platform implementations, and Barracuda neural network inference libraries.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• Korean Journal of Applied Biomechanics
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• v.21 no.1
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• pp.85-95
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• 2011

Three-dimensional(3D) motion analysis is a useful tool for analyzing sports performance. During the last few decades, advances in motion analysis equipment have enabled us to perform more and more complicated biomechanical analyses. Nevertheless, considering the complexity of biomechanical models and the amount of data recorded from the motion analysis system, subsequent processing of these data is required for event-specific motion analysis. The purpose of this study was to develop a basic golf swing analysis algorithm using a state-of-the-art VICON motion analysis system. The algorithm was developed to facilitate golf swing analysis, with special emphasis on 3D motion analysis and high-speed motion capture, which are not easily available from typical video camera systems. Furthermore, the developed algorithm generates golf swing-specific kinematic and kinetic variables that can easily be used by golfers and coaches who do not have advanced biomechanical knowledge. We provide a basic algorithm to convert massive and complicated VICON data to common golf swing-related variables. Future development is necessary for more practical and efficient golf swing analysis.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1627-1633
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• 2017

Human dance is difficult to learn since there is no effective way to imitate an expert's motion, a sequence of complicated body movements, without taking an actual class. In this paper, we propose a dance learning system using human depth information. In the proposed system, a set of example motions are captured from various expert dancers through a marker-free motion capture and archived into a motion database server for online dance lessons. Given the end-user devices such as tablet and kiosk PCs, a student can learn a desired motion selected from the database and send one's own motion to an instructor for online feedback. During this learning process, our system provides a posture-based motion search and multi-mode views to support the efficient exchange of motion data between the student and instructor under a networked environment. The experimental results demonstrate that our system is capable to improve the student's dance skills over a given period of time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Physical Therapy Korea
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• v.29 no.4
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• pp.262-268
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• 2022

Background: Virtual reality (VR) programs based on motion capture camera are the most convenient and cost-effective approaches for remote rehabilitation. Assessment of physical function is critical for providing optimal VR rehabilitation training; however, direct muscle strength measurement using camera-based kinematic data is impracticable. Therefore, it is necessary to develop a method to indirectly estimate the muscle strength of users from the value obtained using a motion capture camera. Objects: The purpose of this study was to determine whether the pedaling speed converted using the VR engine from the captured foot position data in the VR environment can be used as an indirect way to evaluate knee muscle strength, and to investigate the validity and reliability of a camera-based VR program. Methods: Thirty healthy adults were included in this study. Each subject performed a 15-second maximum pedaling test in the VR and built-in speedometer modes. In the VR speedometer mode, a motion capture camera was used to detect the position of the ankle joints and automatically calculate the pedaling speed. An isokinetic dynamometer was used to assess the isometric and isokinetic peak torques of knee flexion and extension. Results: The pedaling speeds in VR and built-in speedometer modes revealed a significantly high positive correlation (r = 0.922). In addition, the intra-rater reliability of the pedaling speed in the VR speedometer mode was good (ICC [intraclass correlation coefficient] = 0.685). The results of the Pearson correlation analysis revealed a significant moderate positive correlation between the pedaling speed of the VR speedometer and the peak torque of knee isokinetic flexion (r = 0.639) and extension (r = 0.598). Conclusion: This study suggests the potential benefits of measuring the maximum pedaling speed using 3D depth camera in a VR environment as an indirect assessment of muscle strength. However, technological improvements must be followed to obtain more accurate estimation of muscle strength from the VR cycling test.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.102-108
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• 2007

Among culture industry that greet digital generation and is observed 21th century the most representative game industry latest is caught what and more interest degree is rising. 2D and 3D animation accomplish continuous growth and development depending action expression along with development of computer technology, and 2D and 3D animation practical use extent are trend that is widening the area in TV, movie, GAME industry etc. through computer hardware and fast change of software technology. The trend of latest game graphic is trend that the weight is changing from 2D to 3D by 3D game and activation of 3D game character that raise player's immersion stuff and Control in 2D's simplicity manufacturing game balance for one side. This treatise that is reality of 3D game character to classify kind of (Motion Capture) and 3D character animation, form model the sense put. Recognize that is overview and reality of 3D game character first for this about example, and is considered to efficiency is high game industry and digital contents industry hereafter by proposing kind model classification of 3D game character animation, form model classification data and character animation manufacture process that application is possible at fast time and effect in 3D character animation application are big.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.1
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• pp.195-209
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• 2018

This study reviewed emerging trends in 3D technology adopted in apparel design research and product development for rapid prototyping and effective evaluation of product performance. Based on a literature review, the authors discussed technical advantages, practical merits and limitations, applications, and on-going developmental efforts of the following methodologies focusing on 3D body scanning and 3D motion capture, and 3D virtual fit simulation technologies. Such data-driven technical approaches observed in recent apparel design research and industry practice are expected to increasingly be adopted in the field to improve consumers' satisfaction with functionality, aesthetics, and comfort of a wide range of apparel products that include daily wear, sport apparel and protective clothing.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.1
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• pp.28-36
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• 2002

Regarding efficient development of computer graphic animations, lots of techniques for editing or transforming existing motion data have been developed. Basically, the motion transformation techniques follow optimization process. To make the animation be natural, almost all the techniques utilize kinematics and dynamics in constructing constraints for the optimization. Since the kinematic and dynamic structures of virtual characters to be animated are very complex, the most time-consuming part is known to the optimization process. In order to suggest some guide lines to engineers involved in the motion transformation, in this paper, we analyze the computational complexities for typical motion transformation in quantitative manner as well as the possibility for parallel computation.