• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1764-1768
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• 2004

In the near future, robots will be used for the personal use. To provide useful services to humans, it will be necessary for robots to understand human intentions. Consequently, the development of emotional interfaces for robots is an important expansion of human-robot interactions. We designed and developed an intelligent emotional interface for the robot, and applied the interfaces to our humanoid robot, AMIET. Subsequent human-robot interaction demonstrated that our intelligent emotional interface is very intuitive and friendly

• Journal of Institute of Convergence Technology
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• v.1 no.2
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• pp.1-5
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• 2011

It is known that as many as 15-20 in 10,000 children are diagnosed with autistic spectrum disorder. A framework of the treatment system for children with autism using affective computing technologies was proposed by Chung and Yoon. In this paper, a prototype for the framework is proposed. It consists of emotion stimulating module, multi-modal bio-signal sensing module, treatment module using virtual reality, and emotion recognition module. Primitive experiments on emotion recognition show the usefulness of the proposed system.

• Annual Conference of KIPS
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• 2021.05a
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• pp.331-334
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• 2021

In the latest eras, there has been a significant amount of development and research is done on the usage of Deep Learning (DL) for speech emotion recognition (SER) based on Convolutional Neural Network (CNN). These techniques are usually focused on utilizing CNN for an application associated with emotion recognition. Moreover, numerous mechanisms are deliberated that is based on deep learning, meanwhile, it's important in the SER-based human-computer interaction (HCI) applications. Associating with other methods, the methods created by DL are presenting quite motivating results in many fields including automatic speech recognition. Hence, it appeals to a lot of studies and investigations. In this article, a review with evaluations is illustrated on the improvements that happened in the SER domain though likewise arguing the existing studies that are existence SER based on DL and CNN methods.

• Smart Media Journal
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• v.13 no.7
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• pp.18-26
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• 2024

One of the newest trends in AI is emotion recognition utilizing keystroke dynamics, which leverages biometric data to identify users and assess emotional states. This work offers a comparison of four datasets that are frequently used to research keystroke dynamics: BB-MAS, Buffalo, Clarkson II, and CMU. The datasets contain different types of data, both behavioral and physiological biometric data that was gathered in a range of environments, from controlled labs to real work environments. Considering the benefits and drawbacks of each dataset, paying particular attention to how well it can be used for tasks like emotion recognition and behavioral analysis. Our findings demonstrate how user attributes, task circumstances, and ambient elements affect typing behavior. This comparative analysis aims to guide future research and development of applications for emotion detection and biometrics, emphasizing the importance of collecting diverse data and the possibility of integrating keystroke dynamics with other biometric measurements.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.363-369
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• 2020

This study intends to examine effects of python education for adolescents. 6 primary studies were chosen through careful search process and investigated through meta-analysis. Research findings were as follows. The total effect size was 0.684. Second, the effect sizes of dependent variables were academic achievement 0.871, cognitive domain 0.625, and affective domain 0.428 in order. Third, for cognitive domain, the effect sizes were self-efficacy 0.833, problem-solving 0.283, computing thinking 0.276, and coding competency 0.251 in order. Fourth, for affective domain, the effect sizes were learning interest 0.560 and programming interest 0.417 in order. Fifth, regarding school level, the effect sizes were middle school 0.851, high school 0.585, and college 0.435 in order. Finally, for subject areas, the effect sizes were mathematics 1.057, design 0.595, information 0.585, and software 0.28 in order.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.119-132
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• 2006

The dimensions of affection structure from hand movements was developed for the purpose of understanding relationship between affective words and physical factors to apply it to computing environment. To analyze hand movements, three dimensions -direction, time, weight- were found through reconstructing sub-properties of Laban Movement Analysis. The direction dimension has five freedoms of movement (horizontal, vertical, sagittal, circular, shaking) while the time and weight dimensions both have two sub categories each, (sudden, sustained), (light, strong) respectively. By factorial design using the three dimensions, twenty movement were videotaped. Participants rated a list of fifty korean affective words on each twenty movements. The results were studied by nonlinear principal component analysis. The results suggested that time and weight dimensions are closely related with arousal level dimension of affection. Strong and sudden movements associated with highly aroused affection, while light and sustained movements associated with the opposite affection. The direction sub-dimensions were found to be associated with the kinds of affection. Linear movements like horizontal, vortical and sagittal direction were correlated to highly aroused negative affection. Circular movements were found to correlate closely by fun and delight on the graph, while shaking movements were correlated to anxiety and impatience. These results imply that the dimensions of affection structure and sub-properties of hand movements are closely connected with each other.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.139-148
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• 2019

Fostering science-gifted individuals are very important for the future of the nation, and it is especially important to cultivate information-gifted individuals in the age of the fourth industry. There is no standardized curriculum for each gifted education center of the University. Therefore, in this study, we analyzed how effective the curriculum developed on the basis of computing thinking is to affect the characteristics of the information-gifted individuals. The curriculum developed on the components of computing thinking was applied to the information-gifted students of K University. In order to verify the effectiveness of the curriculum, we developed a creative personality test and an intrinsic motivation test, and conducted tests before and after the training. We compared pre-post test results by t-test with R program. The creative personality test consisted of 36 items with 6 factors: risk-taking, self - acceptance, curiosity, humor, dominance, and autonomy. The intrinsic motivation test consisted of 20 items with 5 items: curiosity and interest oriented tendency, challenging learning task preference orientation, independent judgment dependency propensity, independent mastery propensity, and internal criterion propensity. The effect of the curriculum on the creative personality of the experimental group was significant (0.009, 0.05). The significance level of the intrinsic motivation was 0.056 and was not significant at the 0.05 level of significance.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1171-1180
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• 2019

Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.29-38
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• 2017

Predicting the user's reaction to the game at the stage of developing the game is important because it is related to the popularity of the game. In this paper, we propose a system that can collect and analyze game user's biometric information in a non-invasive way. To this end, we developed a mouse with skin conductance, pressure, gyroscope, and accelerometer sensor using Arduino. In order to verify the usefulness of this system, the subject was experimented with playing the first person shooter game with this mouse. We analyzed the gameplay videos recorded during Blizzard's 'OverWatch' and the bio-information collected from various sensors in the mouse.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.1-21
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• 2012

Conventional studies on technology acceptance have focused on information technology for utilitarian value and hence based on 'theory of reasoned action'. Correspondingly, the studies depend on perceived usefulness and perceived ease of use as rational decision making elements. However, in ubiquitous society, innovative technologies are applied to non-task area, as well as task-oriented area. Therefore, users are more influenced by affective factors than cognitive factors in causing their usage intention. In line with those discussions, we cannot make sure that the conventional technology acceptance model could fully explain the current u-service acceptance phenomenon. Hence, to overcome the limitations of the prior technology acceptance studies, we propose an amended TAM which includes one hedonic factor and two factors on ubiquitous computing technology : ubiquity and intelligence.