• 대한간호학회지
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• 제20권3호
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• pp.381-398
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• 1990

Nursing involves deep human interpersonal relationships between nurses and patients. But in modem Korea, the nurse - patient relationship tends to be ritualistic and mechanestic. Patients usually express the hope that nurses be more tender and kind. Patients expect nurses to express their warmth especially through nonverbal behaviour. This study was conducted to identify patients' preferences for nurse's nonverbal expressions of warmth. Through the confirmation of these preferences, nurses may learn how to enhance their interpersonal relationships with patients. Subjects for the study were 73 patients who had been admitted to a university teaching hospital for at least three days and agreed to be interviewed by the investigator. The interactions were studied nonverbal expressions of warmth during nursing rounds and administration of oral medication. The interview schedule was expecially designed by the investigator to measure the nurse's posture, the distance between the nurse and the patient, the nurse's eye contact, facial expression, hand motion and head nodding. Data analysis included frequencies, percentages and X²-test. The results of this study may be summerized as follows : 1. Patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds. Preferred nurse's posture was sitting(50.7%) or standing(49.3%) opposite the patient. Preferred distance between the nurse and the patient was close to the bed(93.2%), less than 1m. Preferred eye contact was directed to the patient's eyes or their affected part (41.1%). Preferred facial expression was a smile(97.3%). Preferred hand motions were light gestures(41.1%). Patients preferred head nodding which approved their own opinions(69.9%). 2. Patient's preferences for nurse's nonverval expressions of warmth during administration of oral medication. Preferred nurse's posture was standing and waiting to confirm that the medication had been taken(58.9%). Preferred distance from the patient was at arm's length, 0.5-1m(64.4%). Patients preferred direct eye contact(58.9%) and a smile(94.5%). Patients preferred that the nurse put the medicine directly the patient's hand(64.4%). Whether the nurse nodded her head or not was not considered important. 3. The relation of general characteristics and patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds and administration of oral medication. During nursing rounds, the age of subjects(p＝0.010) and the standard of education(p＝0.026) related to the distance between the nurse and the patient. The sick hospital ward related to the eye contact(p＝0.017) and facial expression(p＝0.010). During administration of oral medication, the age of subjects(p＝0.044) and days of hospital treatment (p＝0.043) and the sick hospital ward(p＝0.0004) related to the facial expression. From this study, nurses can learn what kind nonverbal expressions of warmth are preferred by patients during rounds and administration and thus will enhance nurse- patient interpersonal relationships.

• 인지과학
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• 제29권1호
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• pp.39-59
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• 2018

작업기억 부담이 부적 얼굴정서 처리에 미치는 영향을 밝히기 위해, N-back 과제 수행 도중 제시된 부적 얼굴표정에 의해 유발된 ERP성분들을 조사하였다. 한 개씩 순차적으로 제시되는 시각적 사물그림들에 대한 기억을 유지하고 갱신하도록 요구하면서(N-back 과제) 이 사물그림들이 제시되는 사이에 공포표정 또는 중립표정의 얼굴자극을 하나씩 제시하였는데, 작업기억 부담을 0-back 조건(저부담)과 2-back 조건(고부담)으로 조작하였다. N-back과제 수행반응을 분석한 결과, 고부담조건에 비해 저부담조건에서 더 빠르고 정확한 반응이 관찰되었다. 얼굴자극에 의해 유발된 ERP 평균진폭을 분석한 결과, 후두영역에서 측정한 P1 진폭에서는 정서가효과는 유의미하지 않았고 작업기억 부담효과만 유의미하였다(고부담 > 저부담). 후측 후두-측두 영역에서 측정한 N170 진폭에서 얼굴 정서가효과는 전반적으로는 유의미하였지만(부정 > 중립) 세부적으로는 작업기억부담과 반구에 따라 다르게 나타났는데, 정서가효과가 좌반구에서는 저부담조건의 경우에만, 우반구에서는 두 부담조건 모두에서 관찰되었다. 결국, 얼굴표정의 부적 정서가가 N170에 미치는 영향이 좌반구에서는 작업기억 부담에 의해 조절되었지만 우반구에서는 그렇지 않았다. 이러한 결과는, 부적 얼굴 표정의 초기 정서처리가 작업기억의 유지 및 조작 부담이 큰 경우 좌반구에서는 약화되거나 일어나지 않을 수 있는 반면, 우반구에서는 작업기억 부담의 크기에 관계없이 일어남을 보여주는 것으로써, 부적 얼굴정서 처리의 우반구 편재를 시사한다.

• 한국의류산업학회지
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• 제14권5호
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• pp.803-810
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• 2012

This study investigates the effect of nonverbal communication on trust, switching barrier, and repurchase intention. Sample subjects used in this study were customers of a fashion shop in Sunchon. The questionnaires were conveniently sampled from July 2010 to August 2010. Questionnaire data from 335 customers of a national brand were analyzed through a reliability analysis, factor analysis, and multiple regression analysis. The results of this study are as follows. First, nonverbal communication by the service provider was divided into 3 types, physical appearance and paralanguage, postures and proxemics, and facial expressions. Second, it was found that physical appearance and paralanguage, postures and proxemics, facial expression of nonverbal communication had a significant impact on customer trust. Third, given the relationship between nonverbal communication and switching barrier, it was represented that the postures and proxemics and facial expressions (except physical appearance and paralanguage) had a significantly positive influence on the switching barrier. Forth, physical appearance/paralanguage, postures/proxemics, and facial expressions (nonverbal communication) had a positive influence on repurchase intention. Fifth, given the relationship between trust and repurchase intention as well as switching barrier and repurchase intention, it was represented that trust and switching barrier have a significantly positive influence upon repurchase intention. According to the results of this study, the more positive nonverbal communication by the service provider then the higher the customer repurchase intention as well as trust and switching barrier. Fifth, given the relationship between trust and repurchase intention as well as switching barrier and repurchase intention, it was represented that trust and switching barrier have a significantly positive influence upon repurchase intentions.

• 한국전자통신학회논문지
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• 제15권1호
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• pp.85-92
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• 2020

심층학습은 많은 양의 데이터셋을 학습에 활용하여 객체 분류, 검출, 분할 등의 영상 분석에 탁월한 성능을 나타내고 있다. 본 논문에서는 데이터셋의 종류가 다양한 얼굴 표정인식 데이터셋들을 활용하여 학습 데이터셋의 특성이 심층학습 성능에 영향을 줄 수 있음을 확인하고, 각 학습 데이터셋에 적합한 심층학습 모델의 구성 요소를 설정하는 방법을 제안한다. 제안하는 방법은 심층학습 모델의 성능에 영향을 주는 구성 요소인 활성함수, 그리고 최적화 알고리즘을 유전 알고리즘을 이용하여 선정한다. CK+, MMI, KDEF 데이터셋에 대해서 널리 활용되고 있는 심층학습 모델의 각 구성 요소별 다양한 알고리즘을 적용하여 성능을 비교 분석하고, 유전 알고리즘을 적용하여 최적의 구성 요소를 선정할 수 있음을 시뮬레이션을 통하여 확인한다.

• 정보처리학회논문지B
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• 제16B권4호
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• pp.299-308
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• 2009

AAM(Active Appearance Model)은 PCA(Principal Component Analysis)를 기반으로 객체의 형태(shape)와 질감(texture) 정보에 대한 통계적 모델을 통해 얼굴의 특징점을 검출하는 알고리즘으로 얼굴인식, 얼굴 모델링, 표정인식과 같은 응용에 널리 사용되고 있다. 하지만, AAM알고리즘은 초기 값에 민감하고 입력영상이 학습 데이터 영상과의 차이가 클 경우에는 검출 에러가 증가되는 문제가 있다. 특히, 입을 다문 입력얼굴 영상의 경우에는 비교적 높은 검출 정확도를 나타내지만, 사용자의 표정에 따라 입을 벌리거나 입의 모양이 변형된 얼굴 입력 영상의 경우에는 입술에 대한 검출 오류가 매우 증가되는 문제점이 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 입술 특징점 검출을 통해 정확한 입술 영역을 검출한 후에 이 정보를 이용하여 AAM을 수행함으로써 얼굴 특징점 검출 정확성을 향상시키는 방법을 제안한다. 본 논문에서는 AAM으로 검출한 얼굴 특징점 정보를 기반으로 초기 입술 탐색 영역을 설정하고, 탐색 영역 내에서 Canny 경계 검출 및 히스토그램 프로젝션 방법을 이용하여 입술의 양 끝점을 추출한 후, 입술의 양 끝점을 기반으로 재설정된 탐색영역 내에서 입술의 칼라 정보와 에지 정보를 함께 결합함으로써 입술 검출의 정확도 및 처리속도를 향상시켰다. 실험결과, AAM 알고리즘을 단독으로 사용할 때보다, 제안한 방법을 사용하였을 경우 입술 특징점 검출 RMS(Root Mean Square) 에러가 4.21픽셀만큼 감소하였다.

• 재활복지공학회논문지
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• 제11권2호
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• pp.179-186
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• 2017

안면 비대칭은 다양한 원인에 의해 발병되기 때문에 원인 분석이 중요하고, 평가하는데 있어서 정량적인 지표가 필요하다. 본 연구에서는 웹켐을 이용하여 얻은 영상을 영상처리 및 연산부를 거쳐 마커를 추적하고 마커 간의 거리를 계산하여 안면 마비를 평가하는데 정량적인 지표로 사용하던 Nottingham Grading System을 안면 비대칭을 평가하는데 적용해 보았다. 기존 Nottingham Grading System은 표정 변화에 따른 안면부의 특징점 들간의 거리변화를 합산하여 좌, 우를 비교하기 때문에 특정 케이스의 경우 측정 오류를 불러일으키는 문제점이 있었다. 기존 Nottingham Grading System과 문제점을 보완하여 개선시킨 평가지표를 이용하여 안면비대칭인 피실험자와 정상의 피실험자를 비교하였다. 기존 Nottingham Grading System에서는 안면 비대칭의 경우 99.0%, 정상의 경우 95.0%로 둘 다 정상 범위 속에 포함되었다. 하지만 개선시킨 Nottingham Grading System에서는 안면 비대칭의 경우 74.0%, 정상의 경우 93.2%의 결과가 나왔다. 본 연구의 결과로 인해, 개선시킨 Nottingham Grading System은 각 부위별 상세한 평가 및 진단이 가능하고, 기존 Nottingham Grading System의 '문제점을 보완하였음을 보여주었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.295-298
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• 2007

본 논문에서는 사람의 얼굴표정을 통해 4개의 기본감정(기쁨, 슬픔, 화남, 놀람)에 대한 특징을 추출하고 인식하여 그 결과를 이용하여 감정표현 시스템을 구현한다. 먼저 주성분 분석(Principal Component Analysis)법을 이용하여 고차원의 영상 특징 데이터를 저차원 특징 데이터로 변환한 후 이를 선형 판별 분석(Linear Discriminant Analysis)법에 적용시켜 좀 더 효율적인 특징벡터를 추출한 다음 감정을 인식하고, 인식된 결과를 얼굴 표현 시스템에 적용시켜 감정을 표현한다.

• 대한수의학회지
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• 제44권3호
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• pp.335-341
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• 2004

This study was undertaken to examine the developmental expression of osteopontin(OPN) in the mouse brain. In Western blotting analysis, the expression of OPN was noted initially at embryonic stage and increased gradually after birth and decreased at postnatal day 60(P60). In immunohistochemistry, OPN expression was found in the interstitial nucleus Cajal and the substantia nigra reticularis in anterior part of the brain and in the inferior olivary complex, the parabrachial nucleus, the facial nucleus, the gigantocellular reticular nucleus, the trigeminal nucleus and the anterior interposed nucleus in posterior part of the brain at P31 and P60. In addition, OPN expression in widespread neurons appeared during the period of neuronal differentiation, increased just after birth and decreased with maturation. These results suggest that OPN contributes to developmental processes, including the differentiation and maturation of specific neuronal populations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.