• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.37-46
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• 2021

In this paper, we designed and implemented a program to measure and to judge the accuracy of yoga postures using Azure Kinect. The program measures all joint positions of the user through Azure Kinect Camera and sensors. The measured values of joints are used as data to determine accuracy in two ways. The measured joint data are determined by trigonometry and Pythagoras theorem to determine the angle of the joint. In addition, the measured joint value is changed to relative position value. The calculated and obtained values are compared to the joint values and relative position values of the desired posture to determine the accuracy. Azure Kinect Camera organizes the screen so that users can check their posture and gives feedback on the user's posture accuracy to improve their posture.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.268-269
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• 2021

본 논문에서는 RGB이미지와 Depth 이미지를 촬영할 수 있는 촬영 장비인 Azure Kinect를 사용해 다시점 촬영 시스템 구성을 위한 카메라 동기화 시스템을 제안한다. 제안한 시스템에는 8대의 Azure Kinect 카메라를 사용하고 있으며 각 카메라는 3.5-mm 오디오 케이블로 연결되어 외부동기화 신호를 전달한다. 그리고 이미지를 저장할 때 발생하는 메모리에서의 병목현상을 최소화하기 위해 촬영 시스템의 동작을 16개의 버퍼로 나누어 병렬 컴퓨팅으로 진행한다. 이후 동기화 여부에 따른 차리를 디바이스 타임스탬프를 기준으로 하여 비교한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.367-372
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• 2021

The purpose of this study is to suggest that the movement expression activity of intellectually disabled people is effective in the learning process of LMA motion recognition based on Kinect sensor. We performed an ICT motion recognition games for intellectually disabled based on movement learning of LMA. The characteristics of the movement through Laban's LMA include the change of time in which movement occurs through the human body that recognizes space and the tension or relaxation of emotion expression. The design and implementation of the motion recognition model will be described, and the possibility of using the proposed motion recognition model is verified through a simple experiment. As a result of the experiment, 24 movement expression activities conducted through 10 learning sessions of 5 participants showed a concordance rate of 53.4% or more of the total average. Learning motion games that appear in response to changes in motion had a good effect on positive learning emotions. As a result of study, learning motion games that appear in response to changes in motion had a good effect on positive learning emotions

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.181-190
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• 2021

Sports activities conducted by multiple people are difficult to proceed in a situation where a widespread epidemic such as COVID-19 is spreading, and this causes a lack of physical activity in modern people. This problem can be overcome by using online exercise contents, but it is difficult to check detailed postures such as during face-to-face exercise. In this study, we present a model that detects posture and tracks movement using IT system for better non-face-to-face exercise content management. The proposed motion tracking model defines a body model with reference to motion analysis methods widely used in physical education and defines posture and movement accordingly. Using the proposed model, it is possible to recognize and analyze movements used in exercise, know the number of specific movements in the exercise program, and detect whether or not the exercise program is performed. In order to verify the validity of the proposed model, we implemented motion tracking and exercise program tracking programs using Azure Kinect DK, a markerless motion capture device. If the proposed motion tracking model is improved and the performance of the motion capture system is improved, more detailed motion analysis is possible and the number of types of motions can be increased.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.239-241
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• 2021

2019 발생한 COVID-19로 인하여 전 세계 사람들의 여가 활동이 제한되면서 건강관리를 위해 홈 트레이닝에 많은 관심을 기울이고 있다. 뿐만 아니라 최근 컴퓨팅 기술의 발전에 따라 사람의 행동을 눈으로 직접 판단했던 작업을 컴퓨터가 키포인트 탐지를 통해 인간의 행동을 이해하려는 많은 연구가 진행되고 있다. 이에 따라 본 논문은 Azure Kinect를 이용하여 촬영한 RGB 이미지와 Depth 이미지를 이용하여 3D 키포인트를 추정한다. RGB 이미지는 2D 키포인트 탐지기를 이용하여 2차원 공간에서의 좌표를 탐지한다. 앞서 탐지한 2D 좌표를 Depth 이미지에 투영하여 추출한 3D 키포인트의 깊이 값을 이용하여 3D 키포인트 탐지에 대한 연구 개발하였다.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.6
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• pp.382-390
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• 2021

The present study suggests the application of a depth camera for wave height field measurement, focusing on the calibration procedure and test setup. Azure Kinect system is used to measure the water surface elevation, with a field of view of 800 mm × 800 mm and repetition rate of 30 Hz. In the optimal optical setup, the spatial resolution of the field of view is 288 × 320 pixels. To detect the water surface by the depth camera, tracer particles that float on the water and reflects infrared is added. The calibration consists of wave height scaling and correction of the barrel distortion. A polynomial regression model of image correction is established using machine learning. The measurement results by the depth camera are compared with capacitance type wave height gauge measurement, to show good agreement.

• Clinics in Shoulder and Elbow
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• v.26 no.2
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• pp.126-130
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• 2023

Background: Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait. Methods: The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing. Results: Mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion-extension and pronation-supination, respectively. Mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion-extension, rotation, and abduction-adduction, respectively. Conclusions: The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.320-322
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• 2021

With the development of wireless Internet technology and the expansion of the game market, various forms of games are being developed that are mounted on various platforms, including mobile platforms. In this environment, ensuring the immersion of the game user's perspective will secure the game's competitiveness, so it is necessary to increase the immersion by satisfying each area presented by the Human Computer Interaction (HCI) theory. To this end, this high school implemented downhill games and experimented with kiosks by applying an interface that recognizes the human body's movements as a way to secure freedom and immersion of game users.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.