• PNF and Movement
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• v.15 no.2
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• pp.195-200
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• 2017

Purpose: This study attempts to identify the effects of stretching and core exercise using proprioceptive neuromuscular facilitation (PNF) on the pain and functional disability index of patients with chronic lower back pain. Methods: A total of 20 patients with chronic lower back pain were randomly divided into either the experimental group (n=10), who received PNF stretching and core exercise, or the control group (n=10), who received conventional physiotherapy. Both interventions were applied three times a week for six weeks. The visible analogue scale (VAS) was measured in order to determine the level of pain, while the Oswestry Disability Index (ODI) was used to measure the change in the functional disability index. We conducted a paired t-test to compare the within-group change before and after the intervention. To compare the between-group difference, we used an independent t-test. The statistical significance level was set at ${\alpha}=0.05$ for all the variables. Results: The experimental group showed a significant within-group change in both the VAS and the ODI (p<0.01). The control group also showed a significant change (p<0.01). A significant difference was observed between the experimental group and the control group with regard to the change in both the VAS and the ODI after the interventions (p<0.01). Conclusion: In this study, the application of stretching and core exercise using PNF for subjects who complain of chronic lower back pain proved effective in reducing both pain and functional disability. We therefore expect that this intervention can be applied in the future as a useful program for patients with chronic lower back pain.

• PNF and Movement
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• v.14 no.2
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• pp.83-91
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• 2016

Purpose: The purpose of this study was both to examine the effects of proprioceptive neuromuscular facilitation (PNF) lower leg taping and treadmill training on the gait and balance abilities of patients with hemiplegia resulting from a stroke and to provide a taping method based on the PNF concept. Methods: Twenty patients with hemiplegia resulting from a stroke were randomly and equally assigned to a control group (n=10), which received treadmill training, and to an experimental group (n=10), which received PNF lower leg taping and treadmill training. The intervention was conducted five times per week for six weeks. In order to measure changes in the gait ability of the subjects, a 10-meter walking test (10MWT) and a 6-minute walking test (6MWT) were conducted, and in order to measure changes in the subjects' balance ability, a timed up and go test (TUG) was performed. In order to compare differences within each group before and after the intervention, a paired-t test was carried out, and in order to compare differences between the two groups, the analysis of covariance was utilized. All statistical significance levels were set at ${\alpha}=0.05$. Results: There were significant differences before and after the intervention within both groups in changes of 10MWT, 6MWT, and TUG (p<0.01). Regarding differences between the two groups, the experimental group underwent more effective changes than the control group in 6MWT and TUG (p<0.05). Conclusion: This study applied PNF lower leg taping and treadmill training to patients with hemiplegia resulting from a stroke, and this resulted in improvement in the subjects' gait and balance abilities. Taping and treadmill training based on the PNF concept is considered to be usefully applied as one of the programs to improve hemiplegic patients' gait and balance abilities.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.5
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• pp.664-672
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• 2015

In order to develop new immuno-stimulating ingredients from mature leaves of green tea, crude polysaccharides were isolated from pectinase digests of tea leaves (green tea enzyme digestion, GTE-0), after which their immuno-stimulating activities and chemical properties were examined. GTE-0 mainly contained neutral sugars (54.9%) such as glucose (14.2%), arabinose (12.2%), rhamnose (11.1%), and galacturonic acid (45.1%), which are characteristic of pectic polysaccharides. The anti-complementary activity of GTE-0 was similar to that of polysaccharide K (used as positive control). Number of morphologically activated macrophages was significantly increased in the GTE-0-treated group. GTE-0 significantly augmented $H_2O_2$ and reactive oxygen species production by murine peritoneal macrophage cells in a dose-dependent manner, whereas production of nitric oxide showed the highest activity at a dose of $100{\mu}g/mL$ among all tested concentrations. Murine peritoneal macrophages stimulated with GTE-0 showed enhanced production of various cytokines such as interleukin (IL)-6, IL-12, and tumor necrosis factors-${\alpha}$ in a dose-dependent manner. Further, GTE-0 induced higher phagocytic activity in a dose-dependent manner. In ex vivo assay for cytolytic activity of murine peritoneal macrophages, GTE-0-treated group showed significantly higher activity compared to the untreated group at an effector-to-target cell ratio of 20. The above results lead us to conclude that polysaccharides from leaves of green tea have a potent immuno-stimulating effect on murine peritoneal macrophage cells.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.