• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.35-43
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• 2024

This study aimed to objectively classify the lateral torso posture types and functions of older women. We used 3D body scan data of 119 women aged 70-85 years from the 6th SizeKorea project. First, we defined three torso axes to represent the lateral torso posture types: posterior waist-back, back-cervical, and whole torso axes. Next, we asked experts to select one of four lateral torso posture types-stooped, straight, leaning back, and swayback postures-by looking at the lateral photographic data of 119 older women. To identify the axis that best represented each lateral torso posture type, a discriminant analysis was conducted using the angle of each of the three torso axes as an independent variable and an expert's visual classification as a dependent variable. Based on the analysis, the whole torso and backcervical axis angles were selected as variables for judging lateral torso posture types. Subsequently, we developed a classification function to determine which of the four lateral torso posture types of a particular participant was applicable for a new individual. The method developed in this study is significant in that it enables the objective classification of the lateral torso postures types of older women.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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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.

• Journal of the Korean Home Economics Association
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• v.41 no.11
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• pp.1-9
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• 2003

The somatotype classification of this study was to manufacture well-fitted clothes for middle-aged women. The somatotype classification of the upper body of lateral view was based on previous studies, and 4 postures, straight posture, leaning back posture, bent forward posture, and swayback posture were selected for this study. The front of leaning back posture was longer and wider than that of straight posture, and its front neck depth was deeper. Its front interscye breadth was wider and back interscye breadth was narrower. S.N.P. B.P. front waistline length, waist front length, front diagonal length, chest shedder length, front waistline shoulder line length were longer, and S.N.P. scapular back waistline length, back length, back shoulder length, back diagonal length, shoulder line back waistline length were shorter. On the contrary, the front of bent forward posture was shorter and narrower than that of straight posture, and its back neck depth was deeper. The properties of swayback posture were similar to those of bent forward posture. Its front was shorter and narrower, but the results of front neck depth and back neck depth were like those of straight posture.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.437-440
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• 1997

The association of poor body postures with pains or symptoms of musculoskeletal discorders has been reported by many researchers. An ergonomic evaluation of postural stresses as well as biomechanical stresses is also important especially when a job involves highly repetitive or prolonged poor body postures. The human body is divided into five parts: shoulder/upper arm, lower arm/wrist, back, neck, lower extremities. A work-sampling based macropostural classification system was developed to characterize various postures in this study. Application of the posture classification schema developed in this study to 7 automobile assembly tasks showed that the schema can be used as a tool to didntify the operation and tasks involving highly stressful body postures. This posture classification schema can also be applied as a basis for quantitive evaluating the workload of manual task.

• Journal of the Korean Society of Safety
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• v.20 no.2 s.70
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• pp.127-132
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• 2005

The purpose of this study is to compare representative posture classification schemes of OWAS, RULA and REBA in terms of correctness for postural load. The comparison was based on the evaluation results by the three methods for 224 working postures sampled from steel, electronics, automotive, and chemical industries. The results showed that OWAS and REBA generally underestimated postural stress than RULA irrespective of industry type, work performed and whether or not leg posture is balanced. While about $71\%\;and\;73\%$ of the 224 posture were evaluated with the action category/level 1 or 2 by OWAS and REBA respectively, about $60\%$ of the postures were classified into the action level of 3 or 4 by RULA. The coincidence rate of postural stress category between OWAS and RULA was just $33.5\%$, while the rate between RULA and REBA was $46.0\%$. It is concluded from the findings of this study and the previous research that compared to OWAS and REBA, RULA more precisely evaluates postural stress.

• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.39-46
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• 2007

The purpose of this study was to develop a comprehensive posture classification scheme considering the effects of external load and motion repetition as well as those of working posture. The scheme was developed based on a series of existing empirical studies dealing with postural classification scheme, effects of external load and motion repetition. Ranges of joint motions, external load and motion repetition were divided into the groups with the same degree of discomforts. Each group was assigned a numerical relative discomfort score of code on the basis of discomfort values for the neutral position of elbow flexion. The criteria for evaluating stress of working postures were proposed based on the four distinct action categories, in order to enable practitioners to apply appropriate corrective actions. The proposed scheme was compared with OWAS, RULA and REBA. The comparison revealed that while the proposed scheme and RULA showed similar results for the working postures with light external load and non-repetitive postures, the former overestimated postural load for postures with moderate or heavy external load and repetitive postures than the latter.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.73-78
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• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.463-476
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• 2022

In this paper, we propose a table tennis posture classification system using a cooperative robot to develop a table tennis robot that can be trained like a real game. The most ideal table tennis robot would be a robot with a high joint driving speed and a high degree of freedom. Therefore, in this paper, we intend to use a cooperative robot with sufficient degrees of freedom to develop a robot that can be trained like a real game. However, cooperative robots have the disadvantage of slow joint driving speed. These shortcomings are expected to be overcome through quick recognition. Therefore, in this paper, we try to quickly classify the opponent's posture to overcome the slow joint driving speed. To this end, learning about dynamic postures was conducted using image data as input, and finally, three classification models were created and comparative experiments and evaluations were performed on the designated dynamic postures. In conclusion, comparative experimental data demonstrate the highest classification accuracy and fastest classification speed in classification models using MLP (Multi-Layer Perceptron), and thus demonstrate the validity of the proposed algorithm.