• 대한인간공학회지
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• 제8권2호
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• pp.19-26
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• 1989

This study, as a part of integrated human-robot ergonomics, improves the robot motion control on the robot task in the TOES/WCS whose purpose is improving the teaching task constructed in the previous study. First, the updated combined fuzzy process using a new membership function with Weber's law is constructed for the purpose of coordinate reading of the end points in the macro motion control. Second, an algorithm using the geometric analysis is desinged in order to calculate position values and posture values of the robot joints. Third, the MGSLM method is designed to remove unnecessary the robot motion control caused by the GSLM method in the micro motion control. Consequently, proposed methods in this study lessen burdcn of a human of an improvement of the robot motion control and reduce the teaching time of a human operator and inaccuracy of the teaching task, which contribute to the integrated human-robot ergonomics.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권2호
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• pp.131-143
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• 2009

인간이 최소 에너지를 소비하면서 자연스럽고 안정된 상태로 보행한다는 것에 착안하여, 본 논문은 인간 보행의 COG 궤적을 분석함으로써 인간형 이족로봇의 자연스러운 보행에 관한 연구를 수행한다. 이를 위해서 본 논문은 ssgittal plane과 frontal plane상의 보행 영상으로부터 계측된 COG 궤적을 고려함으로써 안정적이고 에너지를 최소화하는 자연스러운 이족로봇의 보행 패턴을 생성한다. 인간과 이족 로봇은 기구학적인 형태는 유사하지만 자유도에 있어 많은 차이를 의이기 때문에 추출된 인간 관절의 회전력을 이족로봇에 바로 적응할 수 없다. 본 논문에서는 인간의 보행패턴으로부터 계측된 각 관절의 회전력과 인간의 COG 궤적으로부터 계산된 ZMP을 이용하는 적응적 보행 패턴 생성 GA 알고리즘을 통해 5-link 이족로봇 모델의 보행패턴을 생성한다. 제안된 알고리즘은 인간의 각 관절의 회전력과 ZMP 궤적을 고려하기 때문에 인간과 같이 유연하고 에너지 소비를 최소화하는 인간형 이족로봇의 보행패턴을 생성한다. 본 논문은 역기구학을 이용한 일반적인 방법과 제안하는 방법에 의해 생성된 보행 패턴을 각각 5-link 이족로봇 모델에 적용하여 시각적인 측면과 에너지 효율측면에서 평가함으로써 제안하는 알고리즘의 우수성을 입증하였다.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• 한국컴퓨터정보학회논문지
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• 제23권8호
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• pp.9-16
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• 2018

In this paper, a kidnapping detection scheme in which human pose estimation is used to classify accurately between kidnapping cases and normal ones is proposed. To estimate human poses from input video, human's 10 joint information is extracted by OpenPose library. In addition to the features which are used in the previous study to represent the size change rates and the regularities of human activities, the human pose estimation features which are computed from the location of detected human's joints are used as the features to distinguish kidnapping situations from the normal accompanying ones. A frame-based kidnapping detection scheme is generated according to the selection of J48 decision tree model from the comparison of several representative classification models. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, the detection accuracy of our newly proposed scheme is compared with that of the previous scheme. According to the experiment results, the proposed scheme could detect kidnapping situations more 4.73% correctly than the previous scheme.

• 한국자동차공학회논문집
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• 제11권5호
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• pp.201-209
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• 2003

Virtual human models are widely used to save time and expense in vehicle safety studies. A human model is an essential tool to visualize and simulate a vehicle driver in virtual environments. This research is focused on creation and application of a human model fer virtual reality. The Korean anthropometric data published are selected to determine basic human model dimensions. These data are applied to GEBOD, a human body data generation program, which computes the body segment geometry, mass properties, joints locations and mechanical properties. The human model was constituted using MADYMO based on data from GEBOD. Frontal crash and bump passing test were simulated and the driver's motion data calculated were transmitted into the virtual environment. The human model was organized into scene graphs and its motion was visualized by virtual reality techniques including OpenGL Performer. The human model can be controlled by an arm master to test driver's behavior in the virtual environment.

• ETRI Journal
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• 제36권4호
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.786-791
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• 2007

Statistical differences between men and women are investigated for a total of eleven joint motions during level walking. Human locomotion which exhibits nonlinear dynamical behaviors is quantified by the chaos analysis. Time series of joint motions was obtained from gait experiments with ten young males and ten young females. Body motions were captured using eight video cameras, and the corresponding angular displacements of the neck and the upper body and lower extremity were computed by motion analysis software. The maximal Lyapunov exponents for eleven joints were calculated from attractors constructed and then were analyzed statistically by one-way ANOVA test to find any difference between the genders. This study shows that sexual differences in joint motions were statistically significant at the shoulder, knee and hip joints.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.296-299
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• 2001

We developed a motion capture system to get angle data of human joints in the walking mode. The motion capture system is a pair of leg-shape device, which is composed of three links with ankle, knee and pelvis joints. The sensors for measurement of the joint angle are potentiometers. We used an A/D converter to get digital data from joint angles, and which are used to simulate and coordinate the biped-walking robot developed in our laboratory. To simulate and analyze walking motion, animation based on three-dimension motion is performed using the open inventor software.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.114-117
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• 2000

We developed a motion capture system to get angle data of human joints in walking mode. The data are used to coordinate the biped-walking robot developed in our laboratory. A pair of motion capture system is composed of three links with the ankle, knee, and pelvis joints. The system has six axes attached with potentiometers. We used an A/D converter was used to get digital data from joint angles. We filterd the data using the Butterworth 4th order digital filter, and simulated walking motion based on the data using the Matlab.

• 한국CDE학회논문집
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• 제15권5호
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• pp.333-342
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• 2010

This paper describes a novel quantitative discomfort evaluation method based on motion data and its application to discomfort analysis of ingress/egress motions for cars. To develop the discomfort evaluation model, we introduced the discomfort regression curve and the range of motion for each degree-of-freedom of the joints of a whole human body. The maximum discomfort value for the joints at a specific time is selected to represent the discomfort value of the whole body at the time. The results of the experiments and questionnaires support the claim that our discomfort measure matches experimental subjective discomfort levels.