• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.459-464
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• 2004

The rifle impact of human body affected by the posture of human for rifling. The interaction human-rifle system influence the firing accuracy. In this paper, impact analysis of human model for shooting posture is carried out. ADAMS code and LifeMOD is used in impact analysis of human model and modeling of the human body, respectively. On the shooting, human model is affected by rifle impact during the 0.001 second. Performed simulation time for shooting is 0.1 second. Applied constraint condition to human-rifle system is rotating and spherical condition. As the results, the displacement of rifle and transfer path analysis of impact of human model is presented.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.216-222
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• 2002

The ergonomic human model can be considered as a tool for the evaluation of ergonomic factors in vehicle design process. The proper anthropometric data on driver's postures are needed in order to apply a human model to vehicle design. Although studies on driver's posture have been carried out for the last few decades, there are still some problems for the posture data to be applied directly to the human model due to the lack of fitness because such studies were not carried out under the conditions for the human model application. In the traditional researches, the joint angles were evaluated by the categorized data, which are not appropriate for the human model application because it is so extensive that it can not explain the posture evaluation data in detail. And the human models require whole-body posture evaluation data rather than joint evaluation data. In this study a postural evaluation function was developed not by category data but by the concept of the loss function in quality engineering. The loss was defined as the discomfort in driver's posture and measured by the magnitude estimation technique in the experiment using a seating buck. Four loss functions for the each joint - knee, hip, shoulder, and elbow were developed and a whole-body postural evaluation function was constructed by the regression analysis using these loss functions as independent factors. The developed postural evaluation function shows a good prediction power for the driver's posture discomfort in validation test. It is expected that the driver's postural evaluation function based on the loss function can be used in the human model application to the vehicle design process.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.561-568
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• 1994

A man model can be used as an effective tool to design ergonomically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movement was known to be a difficult problem. To solve this redundancy problem, a psychophysical cost function was suggested in this study which defines a cost value for each joint movement angle. The psychophysical cost function developed integrates the psychophysical discomfort of joints and the joint range availability concept which has been used for redundant arm manipulation in robotics to predict the arm reach posture. To properly predict an arm reach posture, an arm reach posture prediction model was then developed in which a posture configuration that provides the minimum total cost is chosen. The predictivity of the psychophysical cost function was compared with that of the biomechanical cost function which is based on the minimization of joint torque. Here, the human body is regarded as a two-dimensional multi-link system which consists of four links ; trunk, upper arm, lower arm and hand. Real reach postures were photographed from the subjects and were compared to the postures predicted by the model. Results showed that the postures predicted by the psychophysical cost function closely simulated human reach postures and the predictivity was more accurate than that by the biomechanical cost function.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.219-221
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• 2007

Recent changes to ubiquitous computing requires more natural human-computer(HCI) interfaces that provide high information accessibility. Hand-gesture, i.e., gestures performed by one 'or two hands, is emerging as a viable technology to complement or replace conventional HCI technology. This paper deals with hand-posture recognition. Hand-posture database construction is important in hand-posture recognition. Human hand is composed of 27 bones and the movement of each joint is modeled by 23 degrees of freedom. Even for the same hand-posture,. grabbed images may differ depending on user's characteristic and relative position between the hand and cameras. To solve the difficulty in defining hand-postures and construct database effective in size, we present a method using a 3D hand model. Hand joint angles for each hand-posture and corresponding silhouette images from many viewpoints by projecting the model into image planes are used to construct the ?database. The proposed method does not require additional equations to define movement constraints of each joint. Also using the method, it is easy to get images of one hand-posture from many vi.ewpoints and distances. Hence it is possible to construct database more precisely and concretely. The validity of the method is evaluated by applying it to the hand-posture recognition system.

• Journal of Ocean Engineering and Technology
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• v.22 no.5
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• pp.119-125
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• 2008

Musculoskeletal disorders(MSDs) are one of the major issues in shipbuilding industry. Main risk factors of MSDs include manual handling of heavy weight, awkward posture, repetitive tasks, prolonged static muscle contraction, and so on. in this study, Using the three-dimensional digital human modeling and simulation method we made up a worker and work posture on a virtual environment. To verify this simulation we compared both traditional ergonomic analysis on a real worker and digital program analysis on a digital human. And this paper shows that it is possible to reduce the rate of MSDs in the shipbuilding industry because it means we can change poor posture mid surroundings into better ones.

• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.437-444
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• 2012

The purpose of this study was to suggest appropriate human model for ergonomic evaluation considering working postures on 3D space. Background: Traditionally extreme design rules have been widely utilized at the stage of designing products. Body size of 5th percentile and 95th percentile in stature has been generally selected for controllability and clearance evaluation, respectively. However, these rules had limitations in reflecting working posture in ergonomic evaluation. Method: In order to define working posture on 3D space, not only sagittal plane but also lateral plane was considered. Kinematic linkage body model was utilized for representation of working posture. By utilizing the anthropometric data of 2,836 South Korean male populations, the point cloud for end points of linkage models was derived. The individuals who were lacking in certain controllability were selected as human models for the evaluation. Result: In case of standing posture it was found that conventional approach is proper for all controllability evaluations. Contrary to standing posture, tall people had less controllability on control location below shoulder point in sitting posture. Conclusion: From the derived proper range on controllability, ergonomic evaluation rule was suggested according to working posture especially in standing and sitting. Application: The results of the study are expected to aid in selection of appropriate human model for ergonomic evaluation and to improve the usability of products and work space.

• 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.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1929-1930
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• 2006

Human like robot arm posture for grasping by considering the shape of the target object is quite a challenge in the field of robotics. In this paper, an optimized grasp posture with respect to the shape of the object considering the wrist joint angle and elbow elevation angle, in order to verify that the grasp posture is human like has been proposed. Given a target object, the candidates for grasp are computed by the method described in this paper. For each candidate, the closed loop inverse kinematics has been solved for the corresponding hand position and orientation. From the obtained joint angles through inverse kinematics, the elbow elevation angle has been computed and compared with the elbow elevation angle obtained through human movement data by the characteristic equation. After considering all the candidates, the hand position and orientation with minimum wrist joint and difference in elbow elevation angles has been utilized as the optimized grasp posture. Simulation results are presented.

• 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.