• 대한임베디드공학회논문지
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• 제18권2호
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• pp.41-50
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• 2023

Gait analysis is an important tool in the clinical management of cerebral palsy, allowing for the assessment of condition severity, identification of potential gait abnormalities, planning and evaluation of interventions, and providing a baseline for future comparisons. However, traditional methods of gait analysis are costly and time-consuming, leading to a need for a more convenient and continuous method. This paper proposes a method for analyzing the posture of cerebral palsy patients using only smartphone videos and deep learning models, including a ResNet-based image tilt correction, AlphaPose for human pose estimation, and SmoothNet for temporal smoothing. The indicators employed in medical practice, such as the imbalance angles of shoulder and pelvis and the joint angles of spine-thighs, knees and ankles, were precisely examined. The proposed system surpassed pose estimation alone, reducing the mean absolute error for imbalance angles in frontal videos from 4.196° to 2.971° and for joint angles in sagittal videos from 5.889° to 5.442°.

• Journal of Multimedia Information System
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• 제5권1호
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• pp.15-20
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• 2018

There are a lot of problems in the face detection area. One of them is detecting faces by facial features and reducing number of the false negatives and positions. This paper is directed to solve this problem by the proposed triangle method. Also, this paper explans cascades, Haar-like features, AdaBoost, HOG. We propose a scheme using 12-net, 24-net, 48-net to scan images and improve efficiency. Using triangle method for frontal pose, B and B1 methods for other poses in neural networks are proposed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.257-257
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• 2000

The objective of this research is to monitor and control the vehicle motion in order to remove out the existing safety risk based upon the human-machine cooperative vehicle control. A predictive control method is proposed to control the steering wheel of the vehicle to keep the lane. Desired angle of the steering wheel to control the vehicle motion could be calculated based upon vehicle dynamics, current and estimated pose of the vehicle every sample steps. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net, where not only the state variables, but also the corresponding uncertainties were propagated in forward and backward direction in such a way to satisfy the given constraint condition, maintain consistency, reduce the uncertainties, and guarantee robustness. A series of experiments was conducted to evaluate the control performance, in which a car Like robot was utilized to quit unwanted safety problem. As the results, the robot was keeping very well a given lane with arbitrary shape at moderate speed.

• 한국전자통신학회논문지
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• 제19권1호
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• pp.309-316
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• 2024

얼굴 인식 기술은 다양한 분야에서 활용되고 있지만, 이는 사진 스푸핑과 같은 위조 공격에 취약하다는 문제를 가지고 있다. 이를 극복하기 위한 여러 연구가 진행되고 있지만, 대부분은 멀티모달 카메라와 같은 특별한 장비를 장착하거나 고성능 환경에서 동작하는 것을 전제로 하고 있다. 본 연구는 얼굴 인식 위조 공격 문제를 해결하기 위해, 특별한 장비 없이 일반적인 웹캠에서 동작할 수 있는 LH-FAS v2를 제안한다. 제안된 방법에서는, 머리 자세 추정에는 FSA-Net을, 얼굴 식별에는 ArcFace를 활용하여 사진 스푸핑 여부를 판별한다. 실험을 위해, 사진 스푸핑 공격 비디오로 구성된 VD4PS 데이터셋을 제시하였으며, 이를 통해 LH-FAS v2의 균형 잡힌 정확도와 속도를 확인하였다. 본 방법은 향후 사진 스푸핑 방어에 효과적일 것으로 기대한다.

• 한국산학기술학회논문지
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• 제22권2호
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• pp.127-133
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• 2021

낙상 판단을 위한 최근 발표되는 연구는 RNN(Recurrent Neural Network)을 이용한 낙상 동작 특징 분석과 동작 분류에 집중되어 있다. 웨어러블 센서를 기반으로 한 접근 방식은 높은 탐지율을 제공하나 사용자의 착용 불편으로 보편화 되지 못했고 최근 영상이나 이미지 기반에 딥러닝 접근방식을 이용한 낙상 감지방법이 소개 되었다. 본 논문은 2D RGB 저가 카메라에서 얻은 영상을 PoseNet을 이용해 추출한 인체 골격 키포인트(Keypoints) 정보로 머리와 어깨의 키포인트들의 위치와 위치 변화 가속도를 추정함으로써 낙상 판단의 정확도를 높이기 위한 감지 방법을 연구하였다. 특히 낙상 후 자세 특징 추출을 기반으로 Convolutional Neural Networks 중 Gated Recurrent Unit 기법을 사용하는 비전 기반 낙상 감지 솔루션을 제안한다. 인체 골격 특징 추출을 위해 공개 데이터 세트를 사용하였고, 동작분류 정확도를 높이는 기법으로 코, 좌우 눈 그리고 양쪽 귀를 포함하는 머리와 어깨를 하나의 세그먼트로 하는 특징 추출 방법을 적용해, 세그먼트의 하강 속도와 17개의 인체 골격 키포인트가 구성하는 바운딩 박스(Bounding Box)의 높이 대 폭의 비율을 융합하여 실험을 하였다. 제안한 방법은 기존 원시골격 데이터 사용 기법보다 낙상 탐지에 보다 효과적이며 실험환경에서 약 99.8%의 성공률을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3182-3198
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• 2019

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.160.3-160
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• 2001

The objective of this research is to monitor and control the vehicle motion in order to remove out the existing safety risk based upon the human-machine cooperative vehicle control. A new control method is proposed to control the steering wheel of the vehicle to keep the lane. Desired angle of the steering wheel to control the vehicle motion could be calculated based upon vehicle dynamics, current and estimated pose of the vehicle every sample steps. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net, where not only the state variables, but also the corresponding uncertainties were propagated in ...

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.166-171
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• 2023

With significant advancements in the field of artificial intelligence, many novel algorithms and technologies have emerged. Currently, AI painting can generate high-quality images based on textual descriptions. However, it is often challenging to control details when generating images, even with complex textual inputs. Therefore, there is a need to implement additional control mechanisms beyond textual descriptions. Based on ControlNet, this passage describes a combined utilization of various local controls (such as edge maps and depth maps) and global control within a single model. It provides a comprehensive exposition of the fundamental concepts of ControlNet, elucidating its theoretical foundation and relevant technological features. Furthermore, combining methods and applications, understanding the technical characteristics involves analyzing distinct advantages and image differences. This further explores insights into the development of image generation patterns.

• 복식
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• 제61권9호
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• pp.97-113
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• 2011

To develop the work clothes for the employees in the mechatronics industry in South Korea, a questionnaire survey on many aspects of the work clothes such as type, color, detailed design and function preference of the subject employees has been carried out in the study. The results throughout the survey highlighted certain functions such as extensibility, thermal and air permeability of importance for the work clothing performance. To improve the discomfort caused by some work motions (i.e. squatting down, ascending/descending the stairs, raising arms), more allowances were given to the measurements (i.e. body rise + 2cm and crotch depth + 1cm for squatting down pose; thigh circumference + 1cm and knee circumference + 1cm for ascending/ descending the stairs pose; chest circumference + 5~6cm and jumper length to the level of hip circumference line for raising arms pose) for work clothes patternmaking. The evaluation of the two types of spring/summer and winter work clothes' clothing comfort and wearer mobility suggested certain points to improve the clothing performance (e.g. ventilation slits on the back pleats, arm pits and collar band of the spring/summer suit using net material and lining for the winter suit using quilted thermal materials) and wearer mobility (e.g. pants hem buckles for the easy work motion), which also enabled to fulfill the workers' work clothes design preference.