• PNF and Movement
• /
• v.5 no.2
• /
• pp.73-88
• /
• 2007

Purpose : The purpose of this study was to find out The effect of Low back pain on the total pattens of Proprioceptive neuromuscular facilitation(PNF). Methods : The subjects of the study were 62 low back pain patients. They were divided into two groups: 31 in the experimental group and 31 in the control group. the experimental group performed modalities and PNF total patterns three times for a week. the control group performed only modalities and ROM exercise three times for a week. Back muscle strength was measured by a back muscle strength measuring machine, the intensity of pain was measured by the Visual Analogue Scale (VAS), and the level of disability was measured by Oswestry low back pain disability index. Study measurements were compared before and after 6 weeks exercise program. The two groups of subjects were assessed by utilizing two different balance measurement: Static standing balance was measured by balance performance monitor (BPM) and Dynamic standing balance was measured by one leg standing(OLS). The scale for static standing balance was measured by using, sway area, sway path, max velocity. Results : The results of this study were as follow: 1) The score on visual analogue scale shows statistically significant increase on PNF group of post test(p<.05). 2) The score on Oswestry low back pain disability index. shows statistically significant decrease on PNF group of post test(p<.05). 3) The change sway area was statistically significant on pre-test and post-test(p<.05). 4) The change sway path was statistically significant on pre-test and post-test(p<.05). 5) The change max velocity was statistically significant on pre-test and post-test(p<.05). 6) The score on one leg standing shows statistically significant increase on PNF group of post test(p<.05). Conclusion : These results of this study indicated that Proprioceptive Neuromuscular Facilitation Total patterns which performed for six weeks had a statistically significant influence on low back pain. If the exercise for muscle strength is performed along with therapeutic stabilizing exercise, a better effect can be expected on low back pain. We hope that this study will provide a basic data for further research with a bigger group and on a long-term effect.

• Journal of Music and Human Behavior
• /
• v.11 no.1
• /
• pp.1-19
• /
• 2014

The purpose of this study was to investigate the immediate effect of Patterned Sensory Enhancement (PSE) technique on the motor function of the affected upper limb in hemiplegic stroke patients by comparing the use of PSE and simple rhythmic cue. A total of 16 stroke patients were recruited from rehabilitative hospitals. The participants were assigned to the experimental group (n = 8) and control group (n = 8). While performing six different upper limb motions, musical stimuli applying the PSE technique was presented for the experimental group and simple rhythmic cue using the metronome was applied for the control group. The results showed that while the significantly increased range of motion (ROM) was found in the experimental group with the immediate use of PSE (p < .05), the control group did not show no significant change. This study implies that the use of musical elements in cueing for upper limb motion immediately leads to significant improvement in ROM by providing sufficient temporal, spatial, and dynamic information for expected motor performance.

• The KIPS Transactions:PartA
• /
• v.11A no.2
• /
• pp.195-202
• /
• 2004

This paper introduces the design or parallel Pipeline high-speed analog-to-digital converter(ADC) for the high-resolution video applications which require very precise sampling. The overall architecture of the ADC consists of 4-channel parallel time-interleaved 10-bit pipeline ADC structure a]lowing 200MSample/s sampling speed which corresponds to 4-times improvement in sampling speed per channel. Key building blocks are composed of the front-end sample-and-hold amplifier(SHA), the dynamic comparator and the 2-stage full differential operational amplifier. The 1-bit DAC, comparator and gain-2 amplifier are used internally in each stage and they were integrated into single switched capacitor architecture allowing high speed operation as well as low power consumption. In this work, the gain of operational amplifier was enhanced significantly using negative resistance element. In the ADC, a delay line Is designed for each stage using D-flip flops to align the bit signals and minimize the timing error in the conversion. The converter has the power dissipation of 280㎽ at 3.3V power supply. Measured performance includes DNL and INL of +0.7/-0.6LSB, +0.9/-0.3LSB.

• The KIPS Transactions:PartB
• /
• v.10B no.5
• /
• pp.567-574
• /
• 2003

Application field of third dimension graphic is becoming diversification by the fast development of hardware recently. Various theory of details technology necessary to design game such as 3D MMORPG (Massive Multi-play Online Role Flaying Game) that do with third dimension. Cyber city should be absorbed. It is the detection speed that this treatise is necessary in game engine design. 3D MMORPG game engine has much factor that influence to speed as well as rendering processing because it express huge third dimension city´s grate many building and individual fast effectively by real time. This treatise nay get concept about the collision in 3D MMORPG and detection speed elevation of game engine through improved detection method. Space division is need to process fast dynamically wide outside that is 3D MMORPG´s main detection target. 3D is constructed with tree construct individual that need collision using processing geometry dataset that is given through new graph. We may search individual that need in collision detection and improve the collision detection speed as using hierarchical bounding box that use it with detection volume. Octree that will use by division octree is used mainly to express rightly static object but this paper use limited OSP by limited space division structure to use this in dynamic environment. Limited OSP space use limited space with method that divide square to classify typically complicated 3D space´s object. Through this detection, this paper propose follow contents, first, this detection may judge collision detection at early time without doing all polygon´s collision examination. Second, this paper may improve detection efficiency of game engine through and then reduce detection time because detection time of bounding box´s collision detection.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.26 no.5
• /
• pp.343-349
• /
• 2013

Crashworthy fuel cells have a great influence on improving the survivability of crews. Since 1960's, the US army has developed a detailed military specification, MIL-DTL-27422, defining the performance requirements for rotorcraft fuel cells. In the qualification tests required by MIL-DTL-27422, the crash impact test should be conducted to verify the crashworthiness of fuel cell. Success of the crash impact test means the improvement of survivability of crews by preventing post-crash fire. But, there is a big risk of failure due to huge external load in the crash impact test. Because the crash impact test itself takes a long-term preparation efforts together with costly fuel cell specimens, the failure of crash impact test can result in serious delay of a entire rotorcraft development. Thus, the numerical simulations of the crash impact test has been required at the early design stage to minimize the possibility of trial-and-error with full-scale fuel cells. Present study performs the numerical simulation using SPH(smoothed particle hydro-dynamic) method supported by a crash simulation software, LS-DYNA. Test condition of MIL-DTL-27422 is reflected on analysis and material data is acquired by specimen test of fuel cell material. As a result, the resulting equivalent stresses of fuel cell itself are calculated and vulnerable areas are also evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.398-403
• /
• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.