• 재활복지
• /
• 제14권3호
• /
• pp.225-256
• /
• 2010

본 연구는 초등학교 지적장애아동의 자세조절 특성을 파악하기 위해 시각, 안뜰감각 그리고 몸감각을 조합하여, 실험 조건에 따라 자세유지를 위하여 어떤 감각을 활용하고 있는지, 부적절한 감각자극에 대해서 통합시스템에서 어떻게 반응하는지, 외부 동요에 대해 신경근육의 자세조절 전략 시 동원되는 근육의 수축 개시시간과 개시순서를 각각 측정하여, 지적장애 아동의 자세조절 특성을 살펴보고, 나아가 초등학교 지적장애아동과 일반아동 간에 어떠한 차이가 있는지를 비교분석하여 자세조절능력 향상 프로그램에 적용 시 방향을 제시해주는데 있다. 이를 위하여 성별과 연령을 고려하여 초등학교 지적장애아동 26명과 일반아동 26명의 아동으로부터 자세조절능력을 비교하였다. 그 결과 자세조절과 감각계와의 관계를 파악한 평형점수와 감각활용 분석, 엉덩관절 전략에 의지하는 자세조절 전략, 근수축 개시지연과 근수축 개시순서의 변화 등을 종합해 볼 때 초등학교 지적장애아동의 자세조절의 문제는 운동시스템, 감각시스템, 통합시스템의 자세조절 시스템 전반에 걸친 총체적인 문제로 나타났다. 따라서 초등학교 지적장애아동의 자세조절 문제 해결을 위해서는 운동시스템, 감각시스템, 통합시스템을 포함하는 포괄적 자세조절 훈련 프로그램의 접근이 필요하다는 것을 알 수 있다. 자세조절 프로그램을 구성할 때, 다양한 감각을 제공하여 주고, 제공된 감각을 효율적으로 활용할 수 있는 감각훈련과 함께 고위중추에서 입력된 감각들을 정상적인 자세조절 발달에 활용할 수 있도록 훈련 시켜주어야 한다. 적응 반응을 계획하고 조직화하여 의미 있는 활동을 통해 중추신경계의 처리능력을 향상시키는 치료프로그램이 제공되어야 한다. 또한 자세조절을 안정적으로 할 수 있는 안정성 한계를 넓힐 수 있는 신경발달학적 치료와 함께, 근골격계에서 빠른 근수축 반응과 효율적인 근수축 개시순서를 유발 할 수 있도록 고유수용성 운동조절 프로그램, 자세반응 촉진 프로그램과 같은 훈련이 필요하다고 할 수 있다.

• 한국컴퓨터그래픽스학회논문지
• /
• 제25권1호
• /
• pp.23-32
• /
• 2019

사이버 멀미는 VR 체험 중 발생하는 증상으로, 주로 감각과 인지 시스템 사이의 불일치로 인해 발생하는 것으로 추정된다. 하지만 감각 및 인지 시스템을 객관적으로 측정할 수 있는 방법이 없기 때문에, 사이버 멀미를 측정하는 것은 어렵다. 이를 해결하기 위해 사이버 멀미를 측정하기 위해 다양한 방법론들이 연구되고 있다. 기존의 멀미를 측정하기 위한 방법은 설문방식을 이용하거나, 머신 러닝을 이용하여 뇌파 데이터를 분석하는 방식으로 진행되어 왔다. 하지만 설문을 이용한 방식은 다소 객관성이 떨어지며, 머신 러닝을 사용하는 방식은 아직까지 높은 정확도를 얻은 연구가 부족하다. 본 논문에서는 뇌파 데이터를 Deep Neural Network (DNN) 딥러닝 알고리즘에 적용하여 객관적인 사이버 멀미 측정 방식을 제안한다. 또한 우리는 더 정확한 사이버 멀미 측정 결과를 위하여 딥러닝 네트워크 구조와 뇌파 데이터 전처리 기법을 제안한다. 우리의 접근 방법은 최대 98.88%의 정확도로 사이버 멀미를 측정한다. 또한 우리는 실험에서 사이버 멀미를 유발하는 영상의 특성을 분석한다. 일반적으로 사이버 멀미는 상하 움직임이 심한 화면, 화면의 지속적이고 빠른 전환, 공중에 떠있는 상황에서 발생한다.

• 대한한의학회지
• /
• 제42권4호
• /
• pp.102-119
• /
• 2021

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, using a fully automated facial shape analysis system, we show that facial morphological features are associated with cold pattern. Methods: The facial morphological features calculated from 68 facial landmarks included the angles, areas, and distances between the landmark points of each part of the face. Cold pattern severity was determined using a questionnaire and the cold pattern scores (CPS) were used for analysis. The association between facial features and CPS was calculated using Pearson's correlation coefficient and partial correlation coefficients. Results: The upper chin width and the lower chin width were negatively associated with CPS. The distance from the center point to the middle jaw and the distance from the center point to the lower jaw were negatively associated with CPS. The angle of the face outline near the ear and the angle of the chin line were positively associated with CPS. The area of the upper part of the face and the area of the face except the sensory organs were negatively associated with CPS. The number of facial morphological features that exhibited a statistically significant correlation with CPS was 37 (unadjusted). Conclusions: In this study of a Korean population, subjects with a high CPS had a more pointed chin, longer face, more angular jaw, higher eyes, and more upward corners of the mouth, and their facial sensory organs were relatively widespread.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권9호
• /
• pp.3730-3744
• /
• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• 대한감각통합치료학회지
• /
• 제16권3호
• /
• pp.23-33
• /
• 2018

목적 : 본 연구는 국내 아동 작업치료사들이 사용하고 있는 평가도구를 영역별로 구체화하여, 평가도구의 사용 동향을 파악하고자 하였다. 연구방법 : 본 연구는 아동 작업치료사를 대상으로 평가도구 사용에 대한 설문을 실시하였다. 평가도구의 하위영역은 작업수행, 일상생활활동, 교육, 놀이, 감각-지각, 운동 및 실행, 인지, 사회적 상호작용 기술, 발달, 이학적 검사로 구성하였다. 설문에 응답한 105명의 자료를 대상으로 기술통계 및 빈도분석을 사용하여 분석하였다. 결과 : 본 연구 결과는 초기 평가 시 소요되는 시간은 30~60분(41.9%)이 가장 많았으며, 재평가 주기는 3~6개월(41.0%)로 가장 많이 실시하는 것으로 나타났다. 영역 별 평가도구를 살펴보면, 작업 수행은 Canadian Occupation Performance Measure(55.2%)을, 일상생활활동은 Wee Functional Independence Measure(57.1%)을, 교육은 School Function Assessment(2.5%)를, 놀이 평가도구는 Revised Knox Preschool Play Scale(28.6%)을, 감각-지각은 Developmental Test of Visual Perception(94.3%)을, 운동 및 실행 평가도구는 Bruininks-Oseretsky Test of Motor Proficiency-2(42.9%)를, 인지 평가도구는 Lowenstein Occupational Therapy Cognitive Assessment(11.4%)를, 사회적 상호작용 기술은 Evaluation of Social Interaction(6.1%)을, 발달은 Denver Developmental Screening Test-II(92.4%)를, 이학적 검사는 임상관찰(89.5%)을 주로 사용하는 것으로 나타났다. 결론 : 본 연구를 통하여 국내 아동작업치료사들이 사용하고 있는 평가도구를 영역별로 구체적으로 파악할 수 있었으며, 이를 통해 추후 평가 교육 및 개발을 위한 기초적인 근거자료가 될 것으로 기대된다.

• 유통과학연구
• /
• 제13권5호
• /
• pp.45-52
• /
• 2015

Purpose - Augmented reality (AR) content used in mobile media today can accommodate a wide variety of contextual information. This indicates that making people experience a sense of presence and flow is a very significant factor in augmented reality content. Flow represents a rich immersion potential as representing the progress of emotion and the means to facilitate the operation of the smart phone. Therefore, users will have friendly relational continuance behavior with products and brands that supply this experience. Based on that, the purpose of this study is to investigate the relationships among smart phone AR application traits, presence, flow experience, and relational continuance behavior. First, AR application traits are defined as three categories sensory immersion, navigation, and manipulation, based on preceding studies. This study then examines the influence of AR application traits on the presence and flow experience and looks into the relation among presence, flow experience, and relational continuance behavior. This analysis suggests more detailed and concentrated strategic implications. Research design, data, and methodology - A research model is designed to examine the relation among AR application traits, presence, flow experience, and relational continued behavior. For data collection, questionnaire surveys were composed of multi-items for each component and the direct interview method was used for the interviews. To collect the data, after running the smart phone AR applications, the consumer behaviors of the respondents were generally determined. The questionnaire surveys were conducted for one month, October 2014. A total of 300 questionnaires were distributed with 278 questionnaires used for analysis, excluding the unanswered and insincere questionnaires. The data were analyzed using SPSS ver. 20.0 and LISREL ver. 8.51. Results - The following results are found: First, AR application traits have a significantly positive effect on presence with sensory immersion, navigation, and manipulation all having a significantly positive effect. Second, sensory immersion and manipulation among the AR application traits have a significantly positive effect on flow. However, navigation did not have a significantly positive effect on flow. Third, presence has a significantly positive effect on flow and has a significantly positive effect on relational continuance behavior. Moreover, flow also has a significantly positive effect on relational continuance behavior. This behavior tends to be formed since brands want to encourage relational continuance behavior and positive emotions with the brands being used. Relational continuance behavior accompanies repeat purchasing, positive word-of-mouth and recommendation activities, and forms of trust with the brand. Conclusions - The research results showed that smart phone AR traits had significantly positive effect on presence, flow, and relational continuance behavior. Based on this, smart phone AR application providers should establish an aggressive marketing strategy to accommodate more realistic problems in order to positively influence user behavior. Additionally, the marketers should make efforts to provide fun or convenience in the AR application operation process of the user.

• 대한간호학회지
• /
• 제18권2호
• /
• pp.197-210
• /
• 1988

The purposes of this study were : 1) to assess the level of pain and to identity the varieties and the degree of pain-related behavior, 2) to measure the level of correlation between the level of pain and the degree of pain- related behavior, 3) to test the correlation between the Korean Pain Rating Scale (KPRS) and Graphic Rating Scale(GRS), and 4) to gather data relevant to the Socio-demographic status of the subjects. The level of pain was measured by KPRS and GRS developed by the researchers. The KPRS consists of three dimensions ; the sensory, the affective and the miscellaneous and the GRS of two separate scales ; the intensity scale and the unpleasantness scale. Of the 2, 025 who had visited orthopedic and neurosurgical out-patients department of 11 university hospitals in various districts of Korea with the episode of Joint pain, 405 subjects were self-selected by responding to the data gathering tools and questionaires mailed. The results are summaried as follows : 1. Maale(217, 53.6%) exceeded female patients(188, 46.4%) in number and the onset of joint pain was more prevalent in the age groups of the 20s and the 30s. 160(39.5%) had been hospitalized for the treatment of, and 87(21.5%) had retired because of the joint pain. 2. Mean pain score measured by KPRS was 128.31 (range; 0-1.344.8) ; mean sensory score was 43.23(range ; 0-645.88%), mean affective score was 46.09(range ; 0- 356.72), and mean miscellaneous score was 39.99(range ; 0-341.68). Mean pain scores measured by GRS were ; sensory intensity score ; 109.1(range ; 0-200) and distress score ; 99.1 (range ; 0-200). 3. The prevalent sites of joint pain revealed to be the right knee joint(203; 50.1%), left knee joint(181(44.7%), left ilium(147, 36.3%), lumbar region (106; 26.2%), hip joint(92; 22.7%) and the ankle(84; 20.7%). 4. The average sleep hour was 6.8hours per day and the average rest hour during the day hours was 3.3hours (range 0-20). 5. The average duration of suffering from joint pain was 49.1 months. 6. Most of the subjects(298; 73.6%) used some sorts of pain relieving practices ; the most prevalent pain relieving practice was the compliance with the physician prescribed treatments(34.4%). 7. The level of discomfort in carrying out the ADL(activities of daily living was 101.16(38.83) and the level of needs for aid in carrying out the ADL was 76.62(31.79). 8. The interrelation between KPRS total score and GRS sensory intensity score(.4438), as well as that of GRS distress score(r＝.4446) were not highly correlated, however, sensory and affective dimension within KPRS (.7547) and pain intensity and distress score of GRS(.6975) revealed moderate intercorrelation. 9. Pain-related behaviors such as discomfort in carrying out ADL, the need for aids in carrying out ADL, frequency of pain relieving practices, varieties of pain sites and length of rest hours during the day hours revealed to be highly correlated with the level of pain measured by KPRS, GRS sensory intensity scale and GRS distress scale. The following are recommended ; 1. Test for the correlation of KPRS total score and the summated score of GRS ; sensory intensity and distress scores. 2. Possibilities of utilization of the pain-related behaviors which revealed high correlation as indirect assessment tool for measuring the level of pain.

• 한국측량학회:학술대회논문집
• /
• 한국측량학회 2004년도 춘계학술발표회논문집
• /
• pp.491-500
• /
• 2004

As the applications of 3D GIS rapidly increase, the need for acquisition and continuos update of urban models is increasingly emphasized. Particularly, building models has been considered as the most crucial component of urban models. Many researchers thus have focused on building extraction from mainly aerial photos or airborne LIDAR data but so far mostly failed to achieve satisfactory results in terms of both completeness and precision because of the intrinsic limitation of the sensory data themselves. Therefore, instead of the airborne sensors, we utilize a terrestrial LIDAR to generate precise and complete building models. This paper presents the overview of the sensors for data acquisition, describes data processing methods for building modelling from the acquired data and summerizes the experimental results.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
• /
• pp.41-44
• /
• 2008

In this paper, major species of tree were discriminated in compartment by using LiDAR data and optic imagery. This is an important work in forest field. A current digital stock map has created the aerial photo and collecting survey data. Unlike high resolution imagery, LiDAR data is not influenced by topographic effects since it is an active sensory system. LiDAR system can measure three dimension information of individual tree. And the main methods of this study were to extract reliable the individual tree and analysis techniques to facilitate the used LiDAR data for calculating tree crown 2D parameter. We should estimate the forest inventory for calculating parameter. 2D parameter has need of area, perimeter, diameter, height, crown shape, etc. Eventually, major species of tree were determined the tree parameters, compared a digital stock map.

• 대한임베디드공학회논문지
• /
• 제11권2호
• /
• pp.79-86
• /
• 2016

In wireless sensor networks, increasing the sensing rate of each node to improve the data accuracy usually incurs a decrease of network lifetime. In this study, an energy-adaptive data compression scheme is proposed to efficiently control the sensing rate in an energy-harvesting wireless sensor network (WSN). In the proposed scheme, by utilizing the surplus energy effectively for the data compression, each node can increase the sensing rate without any rise of blackout time. Simulation result verifies that the proposed scheme gathers more amount of sensory data per unit time with lower number of blackout nodes than the other compression schemes for WSN.