• 로봇학회논문지
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• 제15권3호
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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.

• 산업경영시스템학회지
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• 제22권52호
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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.

• 한국산업융합학회 논문집
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• 제26권1호
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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.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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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.

• 스마트미디어저널
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• 제12권11호
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• pp.48-56
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• 2023

3차원 인체자세추정은 스포츠, 동작인식, 영상매체의 특수효과 등의 분야에서 널리 활용되고 있는 기술이다. 이를 위한 여러 방법들 중 다중 시점 3차원 인체자세추정은 현실의 복잡한 환경에서도 정밀한 추정을 하기 위해 필수적인 방법이다. 하지만 기존 다중 시점 3차원 인체자세추정 모델들은 3차원 특징 맵을 사용함에 따라 시간 복잡도가 높은 단점이 있다. 본 논문은 계산 복잡도가 적은 트랜스포머 기반 기존 단안 시점 다중 프레임 모델을 다중 시점에 대한 3차원 인체자세추정으로 확장하는 방법을 제안한다. 다중 시점으로 확장하기 위하여 먼저 2차원 인체자세 검출자 CPN(Cascaded Pyramid Network)을 활용하여 획득한 4개 시점의 17가지 관절에 대한 2차원 관절좌표를 연결한 8차원 관절좌표를 생성한다. 그 다음 이들을 패치 임베딩 한 뒤 17×32 데이터로 변환하여 트랜스포머 모델에 입력한다. 마지막으로, 인체자세를 출력하는 MLP(Multi-Layer Perceptron) 블록을 매 반복 마다 사용한다. 이를 통해 4개 시점에 대한 3차원 인체자세추정을 동시에 수정한다. 입력 프레임 길이 27을 사용한 Zheng[5]의 방법과 비교했을 때 제안한 방법의 모델 매개변수의 수는 48.9%, MPJPE(Mean Per Joint Position Error)는 20.6mm(43.8%) 감소했으며, 학습 횟수 당 평균 학습 소요 시간은 20배 이상 빠르다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권12호
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• pp.517-524
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• 2022

사람의 자세추정(Human pose estimation)은 사람의 관절 키포인트를 추출하여 자세를 추정하는 방법이다. 폐색현상(Occlusion)이 발생하면, 사람의 관절이 가려지므로 관절 키포인트 추출 성능이 낮아진다. 폐색현상은 총 3가지로 행동할 때 스스로 가려짐, 다른 사물에 의해 가려짐과 배경에 의해 가려짐으로 크게 나뉜다. 본 논문에서는 폐색현상 증강기법을 활용하여 효과적인 자세추정방법을 제안한다. 자세추정방법이 지속적으로 연구되어왔지만, 자세추정방법의 가려짐 현상에 관한 연구는 상대적으로 부족한 상태이다. 이를 해결하기 위해 저자는 사람의 관절을 타겟팅하여 의도적으로 가리는 데이터 증강기법을 제안한다. 본 논문에서의 실험 결과는 의도적으로 폐색현상 증강기법을 활용하면 폐색현상에 강인하며 성능이 올라간 것을 보여준다.

• 한국인터넷방송통신학회논문지
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• 제24권3호
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• pp.147-156
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• 2024

본 논문은 장시간 디지털 기기 사용자를 위한 거북목 자세 교정 및 예방 시스템의 설계에 대해서 소개한다. 우리나라의 거북목 환자는 그 수가 2018년부터 2021년까지 13퍼센트 증가하였으며 아직까지 현재 시점의 최신 통계자료에 따르면 호전되지 않은 상황이다. 거북목은 질병 특성상 치료보단 예방이 중요하다. 따라서 본 논문에서는 대부분의 노트북에 있는 내장 카메라 기반의 시스템을 설계하여 시스템의 접근성을 높였으며, Google Mediapipe 오픈소스의 Pose Estimation, Face Landmarks Detection, Iris Tracking, Depth Estimation 등의 기능을 통해 별도의 인공지능 모델이 필요 없도록 설계하여 낮은 비용으로 사용자로 하여금 손쉽게 거북목을 예방하도록 한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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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.

• 산업공학
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• 제23권2호
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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.

• 한국항행학회논문지
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• 제26권4호
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• pp.235-243
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• 2022

본 논문에서는 관성 항법 시스템 (INS)의 경우, 수직편향 (DOV)으로 인한 초기정렬에서의 자세 추정 오차를 분석한다. INS의 속도 및 자세 오차를 기반으로 DOV로 인한 자세 추정 오차를 이론적으로 분석하였다. 이론적 분석을 검증하기 위한 시뮬레이션을 수행했으며 결과는 이론적 분석과 잘 일치했다. 일례로 η=20"일 경우 정렬오차는 ϕ_N=0.00287°, ϕ_U=0.00196°가발생하며, 𝜉=20"일 경우에는 ϕ_E= -0.00286°의 오차가 발생하였다. 이를 통해 INS 자세오차의 결합특성으로 DOV에 기인한 수직 자세오차가 발생함을 확인하였다. 기존의 INS 정렬에서는 고려하지 않았던 DOV로 인해 추가로 자세오차가 발생할 수 있음을 보여 주었으며 이는 고정밀 INS 적용시에 DOV에 대한 보정을 반드시 고려해야 함을 의미한다.