• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.167-173
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• 1998

Many workers like welders work in squatting postures with the object on the ground during an entire work shift. It is suspected that such prolonged squatting without any supporting stool would gradually cause musculoskeletal injuries to workers. This study is to examine the physical stress caused by the prolonged squatting and to recommend a safe work/rest schedule for a welding task with squatting posture based on the lab experiments. In this study, 8 healthy student subjects participated in the experiment. They maintained a squatting work posture for 16 minutes with 4 different stool height conditions: no stool; 10cm height; 15cm height; and 20cm height. Every 2 minutes, the discomfort was subjectively assessed with the magnitude estimation method for the whole body, lower back, upper leg and lower leg. Based on discomfort ratings, we found that a 10cm height stool relieved the workload most. Discomfort rating results also indicated that a 20cm height stool showed the highest workload, and the there were no difference in workload between a 15cm height stool and no stool. We recommend to use low height stools and to maintain such working postures no longer than 6 minutes for prolonged squatting tasks.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.76-83
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• 2004

This study aimed to empirically investigate perceived discomfort depending upon external load, upper limb postures and their holding time. Discomfort was obtained through an experiment, in which external load, wrist flexion/extension, elbow flexion, shoulder flexion and adduction/abduction were used as experimental variables. The subjects were instructed to hold given postures for 60s and to rate their subjective discomfort scores at 5s, 20s, 40s and 60s by using the free modulus method of magnitude estimation. The results showed that while only external load and elbow flexion were statistically significant at the holding time of 5s at ${\alpha}=0.05$ or 0.10, external load and upper limb postures excluding shoulder adduction/abduction significantly affected discomfort ratings at 20s, 40s and 60s at ${\alpha}=0.01$ or 0.05. Discomfort scores were also significantly different between four posture holding times at ${\alpha}=0.01$. The effects of external load and holding time were much larger than those of upper limb postures. Based on the results of this study, it is recommended that external load and holding time as well as working postures betaken into consideration to precisely quantify postural load in industry.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.153-160
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• 2008

One of the most important features of the Mobile Manipulator is redundant freedom. Using it's redundant freedom, a Mobile Manipulator can move in various modes, and perform dexterous motions. In this paper, to improve robot job performance, two robots -mobile robot, task robot- are joined together to perform a job, we studied the optimal position and posture of a Mobile Manipulator to achieve a minimum of movement of each robot joint. Kinematics of mobile robot and task robot is solved. Using the mobility of a Mobile robot, the weight vector of robots is determined. Using the Gradient method, global motion trajectory is minimized, so the job which the Mobile Manipulator performs is optimized. The proposed algorithm is verified with PURL-II which is Mobile Manipulator combined Mobile robot and task robot, and the results are discussed.

• Journal of the Ergonomics Society of Korea
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• v.22 no.1
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• pp.43-56
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• 2003

Observational methods such as OWAS, RULA, and REBA have been widely used to identify posture-related risks of musculoskeletal disorders in industry, since they are useful and efficient in evaluating postural stresses. However. there are few studies comparing the methods and providing guidelines for selecting and using the methods. They have been developed based on different backgrounds and with different application areas. Each method has its own characteristics. which must be considered in selecting and using them. In this study. 17 male subjects evaluated 42 different working postures that frequently assumed in the automobile assembly line using a psychophysical method. The postures were then evaluated by different observational methods. The results of the observational methods were compared with psychophysically evaluated stresses. The observational methods resulted in different values of stresses for certain postures. For some postures showing high values of perceived discomfort. the observational methods showed different values of stresses. These results showed that the observational methods should be used differently according to application area and they have some weak points to be improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.71-76
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• 2019

Research on indoor location tracking technology using smartphone is actively being carried out. Especially, the movement distance of the smartphone should be accurately measured and the movement route of the user should be displayed on the map. Location tracking technology using sensors mounted on smart phones has been used for a long time, but accuracy is not good enough to measure the moving distance of the user using only the sensor. Therefore, when the user moves the smartphone in a certain posture, it must research and develop an appropriate algorithm to measure the distance accurately. In this paper, we propose a method to reduce moving distance estimation error by removing user 's foot shape by limiting the screen of smartphone in pyramid - based optical flow estimation method.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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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°.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.5-7
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• 2021

Motion capture data files for pose estimation may have inaccurate data depending on the surrounding environment and the degree of movement, so it is necessary to correct it. In the past, inaccurate data was restored with post-processing by people, but recently various kind of neural networks such as LSTM and R-CNN are used as automated method. However, since neural network-based data restoration methods require a lot of computing resource, this paper proposes a method that reduces computing resource and maintains data restoration rate compared to neural network-based method. The proposed method automatically restores inaccurate motion capture data by using posture measurement data (c3d). As a result of the experiment, data restoration rates ranged from 89% to 99% depending on the degree of inaccuracy of the data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.135-142
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• 2018

In recent years, researches utilizing UAV for military purposes such as precision tracking and batting have been actively conducted. In order to track the preceding flight, there has been a previous research on estimating the attitude information of the flight such as roll, pitch, and yaw using images taken from the rear UAV. In this study, we propose a method to estimate the attitude information more precisely by applying the Kalman filter to the existing image processing technique. By applying the Kalman filter to the estimated attitude data using image processing, we could reduce the estimation error of the attitude angle significantly. Through the simulation experiments, it was confirmed that the estimation using the Kalman filter can estimate the posture information of the aircraft more accurately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1528-1534
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• 2019

Recently, due to the popularization of 3D depth cameras, new researches and opportunities have been made in research conducted on RGB images, but estimation of human hand pose is still classified as one of the difficult topics. In this paper, we propose a robust estimation method of human hand pose from various input 3D depth images using a learning algorithm. The proposed approach first generates a skeleton-based hand model and then aligns the generated hand model with three-dimensional point cloud data. Then, using a random forest-based learning algorithm, the hand pose is strongly estimated from the aligned hand model. Experimental results in this paper show that the proposed hierarchical approach makes robust and fast estimation of human hand posture from input depth images captured in various indoor and outdoor environments.