• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.269-276
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• 2020

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient's posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.1-8
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• 1999

This study was conducted to collect data concerning the preferred driving postures and adopted seat adjustment levels and to grasp relationships among drivers' body sizes, postural angles, and adopted seat positions and angles. Also optimum driving posture and seat adjustment level estimation models were constructed. An experiment was conducted to investigate observed optimum driving posture, and seat adjustment level. Thirty-six subjects (male=20, female=16) was selected to include a wide range of percentiles in the dimensions important for automotive driving workstation design and to be representative of the automotive driving population in Korea. New guidelines and estimation models for optimum postural comfort were developed. There were significant differences between male and female in postural angles but not in seat adjustment levels. Taller subjects preferred a more open and reclined posture. Estimation models enable us to estimate the quantitative optimum driving posture and seat adjustment level with some drivers' physical dimensions.

• 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.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.167-175
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• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.517-524
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• 2022

Human pose estimation is a method of estimating a posture by extracting a human joint key point. When occlusion occurs, the joint key point extraction performance is lowered because the human joint is covered. The occlusion phenomenon is largely divided into three types of actions: self-contained, covered by other objects, and covered by background. In this paper, we propose an effective posture estimation method using a masking phenomenon enhancement technique. Although the posture estimation method has been continuously studied, research on the occlusion phenomenon of the posture estimation method is relatively insufficient. To solve this problem, the author proposes a data augmentation technique that intentionally masks human joints. The experimental results in this paper show that the intentional use of the blocking phenomenon enhancement technique is strong against the blocking phenomenon and the performance is increased.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.147-156
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• 2024

This paper introduces the design of a turtle neck posture correction and prevention system for users of digital devices for a long time. The number of forward head posture patients in Korea increased by 13% from 2018 to 2021, and has not yet improved according to the latest statistics at the present time. Because of the nature of the disease, prevention is more important than treatment. Therefore, in this paper, we designed a system based on built-camera in most laptops to increase the accessiblility of the system, and utilize the features such as Pose Estimation, Face Landmarks Detection, Iris Tracking, and Depth Estimation of Google Mediapipe to prevent the need to produce artificial intelligence models and allow users to easily prevent forward head posture.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.

• IE interfaces
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• v.23 no.2
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• pp.100-107
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• 2010

For proper ergonomic evaluation using a digital human model simulation (DHMS) system such as $RAMSIS^{(R)}$, the postures of humanoids for designated tasks need to be predicted accurately. The present study (1) evaluated the accuracy of driving postures of humanoids predicted by RAMSIS, (2) proposed a method to improve its accuracy, and (3) examined the effectiveness of the proposed method. The driving postures of 12 participants in a seating buck were measured by a motion capture system and compared with their corresponding postures predicted by RAMSIS. Significant discrepancies ($8.7^{\circ}$ to $74.9^{\circ}$) between predicted and measured postures were observed for different body parts and driving tasks. Two methods (constraints addition and user-defined posture) were proposed and their effects on posture estimation accuracy were examined. Of the two proposed methods, the user-defined posture method was found preferred, reducing posture estimation errors by 11.5% to 84.9%. Both the posture prediction accuracy assessment protocol and user-defined posture method would be of use for practitioners to improve the accuracy of predicted postures of humanoids in virtual environments.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.235-243
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• 2022

In this paper, in the case of an inertial navigation system, the posture estimation error in the initial alignment due to vertical deflection is analyzed. Posture estimation error due to DOV was theoretically analyzed based on the speed and posture error of INS. Simulations were performed to verify the theoretical grinding, and the results were in good agreement. For example, in the case of η=20", an alignment error of ϕ_N=0.00287°, ϕ_U=0.00196° occurred, and in the case of 𝜉=20", an error of ϕ_E= -0.00286° occurred. Through this, it was confirmed that the vertical posture error caused by the DOV occurred as a coupling characteristic of the INS posture error. It has been shown that an additional posture error may occur due to the DOV, which was not considered in the existing INS alignment, which means that correction for the DOV must be considered when applying high-precision INS.