• Title/Summary/Keyword: 정신작업의 정확도

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Variation of reaction time and accuracy of mental work with strength of whole-body activity gradually increasing (강도가 점증하는 전신활동에 따른 반응시간의 변화와 정신작업의 정확도)

  • 김정만
    • Journal of Korea Society of Industrial Information Systems
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    • v.9 no.1
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    • pp.56-62
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    • 2004
  • This paper examined the change in reaction time and accuracy of mental work by physical activity. A treadmill-equipped instrument is used to attain several levels of physical activity. Subjects were recruited from college students and football players; and they were instructed to run on a treadmill at different speeds. In order to determine the individual levels of physical activity of subjects, in this paper, Borg's-RPE scales which indicates subjective levels of physical activity were obtained. And reaction time was evaluated before and after running by arithmetic calculation test Restricted within the limit of this experiment, the results of this study showed that arithmetic calculation performance as a scale of accuracy of mental work rather increase after the exercise even though there are slight difference among the subjects.

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A Study on the Evaluation Model of Mobile Phone Menu Interface (휴대전화 메뉴 인터페이스의 사용시간 예측 모델에 관한 연구)

  • Lee Jee-Su;Paik Doo-Won
    • Proceedings of the Korea Information Processing Society Conference
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    • 2006.05a
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    • pp.727-730
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    • 2006
  • 최근 휴대전화의 보급률이 증가하고, 많은 기능이 집약되면서 휴대전화 인터페이스는 HCI 분야에서 중요한 이슈가 되고 있다. 본 논문에서는 사용자 행위 모델링 기법인 GOMS를 사용하여 휴대전화의 계층 메뉴 구조를 분석하고 사용 시간을 예측하는 개선된 모델을 제시한다. 기존 연구에서는 계층메뉴 구조 내의 모든 작업을 숙련된 작업으로 가정하여 사용 시간 예측모델을 구성하였다. 하지만 실제로 휴대전화의 계층 메뉴 구조 내에는 사용자에게 숙련이 되는 작업과 숙련되지 않는 작업이 섞여 있기 때문에 모든 작업을 숙련된 작업으로 가정할 경우 정확한 사용 시간의 예측을 기대하기 힘들다. 본 논문에서는 계측 메뉴 구조의 정확한 사용시간 예측을 위해, 계층메뉴 상의 작업을 숙련된 작업과 숙련되지 않은 작업으로 분리하여 별도로 사용시간을 예측할 수 있는 방법을 제시한다. 이를 위하여 사용시간 예측모델에 필요한 정신적 준비시간(Mental Operator)의 종류를 제시하였으며 실험을 통해 이를 검증하였다.

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A Concet Analysis of Psychiatric Nurse's Compassionate Communication Competence: Hybrid Model (혼종모형을 이용한 정신 간호사의 공감적 의사소통역량 개념분석)

  • Won Hee Jun;Hye Suk Im
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.6
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    • pp.813-825
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    • 2023
  • This study was analyzed using a mixed methods approach to clarify the concept of compassionate communication competencies of psychiatric nurses. In the theoretical phase, the literature published from 2000 to 2022 was collected and 38 articles were analyzed. For the fieldwork phase, in-depth interviews were conducted with eight psychiatric nurses from December 1 to December 28, 2022. In the final analysis phase, the dimensions and attributes of psychiatric nurses' compassionate communication competence were identified and conceptualized. Based on the attributes identified in the theoretical and fieldwork phases, the definition of psychiatric nurses' compassionate communication competence was synthesized into five dimensions and 12 attributes. Therefore, psychiatric nurses' compassionate communication competence refers to the skills and abilities of psychiatric nurses to use active listening and empathic skills for effective communication based on compassion and understanding of the target, to be sensitive to the thoughts and feelings of the target, to accurately convey what the target wants to express, to respect the target, and to empower the target.

Development of interactive self-system based on artificial intelligent speaker for treatment of children with developmental disabilities (발달 장애 아동 치료를 위한 인공지능 스피커 기반 대화형 자가 시스템 개발)

  • Wee, YeJin;Kye, SeulA;Bae, SeoYeon;Choi, SeoungPyo;Lee, OnSeok
    • Proceedings of the Korea Information Processing Society Conference
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    • 2019.10a
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    • pp.1151-1152
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    • 2019
  • 발달 장애는 신체 및 정신이 해당하는 나이에 맞게 발달하지 않은 상태로, 다른 아동에 비해 신경정신과적 질환 발생 확률이 높기 때문에 발달장애 아동의 치료는 매우 중요하다. 그러나 주관적 판단에 의해 이루어지는 기존 작업치료의 경우, 정량적 성과 지표를 확인하기 힘들고 대상자 스스로 지속적으로 진행하기에 한계가 있다. 본 연구에서는 치료 모델을 가상 공간상에 구현하여 공간에 구애받지 않고 치료를 진행할 수 있으며, 수행 결과에 대한 자료를 정확하고 지속적으로 기록하며 확인할 수 있도록 하였다. 또한, AI 스피커를 통해 치료에 대한 피드백을 줌으로써, 대상자 스스로 실시하여 치료자의 개입을 줄여 심리적 부담을 덜어 더욱 정확한 수행이 이루어지도록 하였다.

Deep Learning Model for Mental Fatigue Discrimination System based on EEG (뇌파기반 정신적 피로 판별을 위한 딥러닝 모델)

  • Seo, Ssang-Hee
    • Journal of Digital Convergence
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    • v.19 no.10
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    • pp.295-301
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    • 2021
  • Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

Effects of Emotional Information on Visual Perception and Working Memory in Biological Motion (정서 정보가 생물형운동자극의 시지각 및 작업기억에 미치는 영향)

  • Lee, Hannah;Kim, Jejoong
    • Science of Emotion and Sensibility
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    • v.21 no.3
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    • pp.151-164
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    • 2018
  • The appropriate interpretation of social cues is a crucial ability for everyday life. While processing socially relevant information, beyond the low-level physical features of the stimuli to emotional information is known to influence human cognition in various stages, from early perception to later high-level cognition, such as working memory (WM). However, it remains unclear how the influence of each type of emotional information on cognitive processes changes in response to what has occurred in the processing stage. Past studies have largely adopted face stimuli to address this type of research question, but we used a unique class of socially relevant motion stimuli, called biological motion (BM), which depicts various human actions and emotions with moving dots to exhibit the effects of anger, happiness, and neutral emotion on task performance in perceptual and working memory. In this study, participants determined whether two BM stimuli, sequentially presented with a delay between them (WM task) or one immediately after the other (perceptual task), were identical. The perceptual task showed that discrimination accuracies for emotional stimuli (i.e., angry and happy) were lower than those for neutral stimuli, implying that emotional information has a negative impact on early perceptual processes. Alternatively, the results of the WM task showed that the accuracy drop as the interstimulus interval increased was actually lower in emotional BM conditions than in the neutral condition, which suggests that emotional information benefited maintenance. Moreover, anger and happiness had distinct impacts on the performance of perception and WM. Our findings have significance as we provide evidence for the interaction of type of emotion and information-processing stage.

A Dual Filter-based Channel Selection for Classification of Motor Imagery EEG (동작 상상 EEG 분류를 위한 이중 filter-기반의 채널 선택)

  • Lee, David;Lee, Hee Jae;Park, Snag-Hoon;Lee, Sang-Goog
    • Journal of KIISE
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    • v.44 no.9
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    • pp.887-892
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    • 2017
  • Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

Hybrid Affine Registration Using Intensity Similarity and Feature Similarity for Pathology Detection

  • June-Sik Kim;Ho-Sung Kim;Jong-Min Lee;Jae-Seok Kim;In-Young Kim;Sun I. Kim
    • Journal of Biomedical Engineering Research
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    • v.23 no.1
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    • pp.39-47
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    • 2002
  • The objective of this study is to provide a Precise form of spatial normalization with affine transformation. The quantitative comparison of the brain architecture across different subjects requires a common coordinate system. For the common coordinate system, not only global brain but also a local region of interest should be spatially normalized. Registration using mutual information generally matches the whose brain well. However. a region of interest may not be normalized compared to the feature-based methods with the landmarks. The hybrid method of this Paper utilizes feature information of the local region as well as intensity similarity. Central gray nuclei of a brain including copus callosum, which is used for feature in Schizophrenia detection, is appropriately normalized by the hybrid method. In the results section. our method is compared with mutual information only method and Talairach mapping with schizophrenia Patients. and is shown how it accurately normalizes feature .

An Evaluation Model for Software Usability using Mental Model and Emotional factors (정신모형과 감성 요소를 이용한 소프트웨어 사용성 평가 모델 개발)

  • 김한샘;김효영;한혁수
    • Journal of KIISE:Software and Applications
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    • v.30 no.1_2
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    • pp.117-128
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    • 2003
  • Software usability is a characteristic of the software that is decided based on learnability, effectiveness, and satisfaction when it is evaluated. The usability is a main factor of the software quality. A software has to be continuously improved by taking guidelines that comes from the usability evaluation. Usability factors may vary among the different software products and even for the same factor, the users may have different opinions according to their experience and knowledge. Therefore, a usability evaluation process must be developed with the consideration of many factors like various applications and users. Existing systems such as satisfaction evaluation and performance evaluation only evaluate the result and do not perform cause analysis. And also unified evaluation items and contents do not reflect the characteristics of the products. To address these problems, this paper presents a evaluation model that is based on the mental model of user and the problems, this paper presents a evaluation model that is based on the mental model of user and the emotion of users. This model uses evaluation factors of the user task which are extracted by analyzing usage of the target product. In the mental model approach, the conceptual model of designer and the mental model of the user are compared and the differences are taken as a gap also reported as a part to be improved in the future. In the emotional factor approach, the emotional factors are extracted for the target products and evaluated in terms of the emotional factors. With this proposed method, we can evaluate the software products with customized attributes of the products and deduce the guidelines for the future improvements. We also takes the GUI framework as a sample case and extracts the directions for improvement. As this model analyzes tasks of users and uses evaluation factors for each task, it is capable of not only reflecting the characteristics of the product, but exactly identifying the items that should be modified and improved.

Analyzing Contextual Polarity of Unstructured Data for Measuring Subjective Well-Being (주관적 웰빙 상태 측정을 위한 비정형 데이터의 상황기반 긍부정성 분석 방법)

  • Choi, Sukjae;Song, Yeongeun;Kwon, Ohbyung
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.83-105
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    • 2016
  • Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.