• PNF and Movement
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• v.17 no.1
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• pp.145-156
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• 2019

Purpose: This study suggests clinical reasoning strategies for therapists with little experience in clinical reasoning for the evaluation and treatment of patients with neurological disorders. Methods: The suggested method was the mnemonic PT STRESS whose initials represent the body structure and functions that can affect the activity limits and the items that can cause problems at the functional level in patients with neurological disorders. Results: PT STRESS stands for pain (P), ability of the trunk (T), sensation (S), tone (T), range of motion (R), emotion and endurance (E), muscular strength (strength), and stability (S). It tests and measures problems in the body structure and functions. Conclusion: This study suggests easy clinical reasoning strategies that can be used by therapists who have insufficient experience in the evaluation or treatment of patients with neurological disorders. However, more factors need to be considered in the future with regard to clinical reasoning of the diverse problems of patients with neurological disorders.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.97-104
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• 2020

This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.421-433
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• 2023

This study examined the reasoning of gifted elementary science students in a socioscientific issues (SSI) classroom discussion on COVID-19-related trash disposal challenges. This study aimed to understand the characteristics of evidence use and decision-making difficulties in each type of SSI-related reasoning. To this end, the transcripts of 17 gifted students of elementary science discussing SSIs in a classroom were analyzed within the framework of informal reasoning. The analysis framework was categorized into three types according to the primary influence involved in reasoning: rational, emotional, and intuitive. The analysis showed that students exhibited four categories of evidence use in SSI reasoning. First, in the rational reasoning category, students deemed and recorded scientific knowledge, numbers, and statistics as objective evidence. However, students who experienced difficulty in investigating such scientific data were less likely to have factored them in subsequent decisions. Second, in the emotional reasoning category, students' solutions varied considerably depending on the perspective they empathized with and reasoned from. Differences in their views led to conflicting perspectives on SSIs and consequent disagreement. Third, in the intuitive reasoning category, students disagreed with the opinions of their peers but did not explain their positions precisely. Intuitive reasoning also created challenges as students avoided problem-solving in the discussion and did not critically examine their opinions. Fourth, a mixed category of reasoning emerged: intuition combined with rationality or emotion. When combined with emotion, intuitive reasoning was characterized by deep empathy arising from personal experience, and when combined with rationality, the result was only an impulsive reaction. These findings indicate that research on student understanding and faculty knowledge of SSIs discussed in classrooms should consider the difficulties in informal reasoning and decision-making.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.34-39
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• 2009

As a subjective recognition effect, human's emotion has impulsive characteristic and it expresses intentions and needs unconsciously. These are pregnant with information of the context about the ubiquitous computing environment or intelligent robot systems users. Such indicators which can aware the user's emotion are facial image, voice signal, biological signal spectrum and so on. In this paper, we generate the each result of facial and voice emotion recognition by using facial image and voice for the increasing convenience and efficiency of the emotion recognition. Also, we extract the feature which is the best fit information based on image and sound to upgrade emotion recognition rate and implement Multi-Modal Emotion recognition system based on feature fusion. Eventually, we propose the possibility of the ubiquitous computing service reasoning method based on Bayesian Network and ubiquitous context scenario in the ubiquitous computing environment by using result of emotion recognition.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.

• Journal of KIISE
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• v.44 no.11
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• pp.1149-1155
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• 2017

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

• Korean Journal of Childcare and Education
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• v.11 no.5
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• pp.293-310
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• 2015

The purpose of this study is to investigate the differences that appeared in the episodes in understandings of the emotional display rules according to the types of emotions and subjects for expressing emotions. In addition, the interaction effects of intention reasoning types and gender on children's understandings of the real emotions and emotional display rules are explored. 144 4-5 year old children in Chungbuk province participated in the experimental interviews. The results are as follows. First, children comprehended the emotional display rules more clearly in a relationship with peers than adults. In terms of a type of emotion, it was the negative emotions rather than positives ones that those children understood better for real emotions and emotional display rules. Second, the main effect of the intention reasoning types on children's understanding of the emotional display rules appeared significant in all episodes. Especially, in negative emotion-peer episode, children with different types of intention reasoning showed a different level of understanding emotional display rules depending on gender of the children.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.137-143
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• 2013

In recent years there have been many efforts for changing from machine oriented technology to human oriented technology gradually. In the research of Intelligent system, the previous simple learning and reasoning methods are also changing to human like processing, namely the direction of implementing humanity. Especially as Neuro Engineering research is getting active, the studies on application of brain function are increasing in the engineering aspects. In this paper, we defined Super Value as a concept which reflect the higher value of 'viewpoint' and proposed flexible KEB(Knowledge-Emotion Binding) System. The system has a hierarchical structure which consists of Main level and Super level for flexibility and it is designed for having the function of extracting KEB Threads by Reasoning mechanism.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.30-33
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• 2002

There are several methods to implement the logical machine reasoning such as a frame theory and a production system of artificial intelligence. And these algorithms can explain the obtained result through the inference processes. However, emotional (KANSEI) patterns are not so easily implement. One of reason is that some emotional expression is the result of process from unconscious level to conscious level, and not easily identified the original unconscious causes. Therefore, a function of KANSEI database needs to structuralize unconscious level. Our approach is to develop the computerized counseling support system which can structuralize the unconscious brain functions from the view point of the psychology with focusing physiological and emotional responses. Especially, development of the algorithm that can form the network from unconscious to conscious using the image recollection is the application of the structured association technique (SAT). The developed system was implemented on the Web using CGI and emotional network database.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.107-111
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• 2005

When a pedagogical agent system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. In this paper, the student model for interactive edutainment applications is proposed. This model is based on Bayesian Networks to expose constructs and parameters of rules and propositions pertaining to game and problem solving activities. This student model could be utilized as the emotion generation model for student and agent as well.