• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.51-58
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• 2010

In the environment of instant messengers, the recognition of human emotions and its automated representation with personalized 3D character animations facilitate the use of affectivity in the machine-based communication, which will contribute to enhanced communication. This paper describes an emotional messenger system developed for the automated recognition and expression of emotions for IPTVs (Internet Protocol televisions). Aiming for efficient delivery of users' emotions, we propose emotion estimation that assesses the affective contents of given textual messages, character animation that supports both 3D rendering and video playback, and smart phone-based input method. Demonstration and experiments validate the usefulness and performance of the proposed system.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.101-112
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• 2017

We investigated the neural representation of reward probability recognition and its neural connectivity with other regions of the brain. Using functional magnetic resonance imaging (fMRI), we used a simple guessing task with different probabilities of obtaining rewards across trials to assay local and global regions processing reward probability. The results of whole brain analysis demonstrated that lateral prefrontal cortex, inferior parietal lobe, and postcentral gyrus were activated during probability-based decision making. Specifically, the higher the expected value was, the more these regions were activated. Fronto-parietal connectivity, comprising inferior parietal regions and right lateral prefrontal cortex, conjointly engaged during high reward probability recognition compared to low reward condition, regardless of whether the reward information was extrinsically presented. Finally, the result of a regression analysis identified that cortico-subcortical connectivity was strengthened during the high reward anticipation for the subjects with higher cognitive impulsivity. Our findings demonstrate that interregional functional involvement is involved in valuation based on reward probability and that personality trait such as cognitive impulsivity plays a role in modulating the connectivity among different brain regions.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of the Korean housing association
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• v.18 no.3
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• pp.103-113
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• 2007

The purpose of this study is calling attention to the silver town by analyse preference attributes diversely and suggest alterative plan. Also, offering basic data which needs for the establishment of comfortable and ideal old age residence culture by developing silver town which is appropriate for our circumstances and emotion. The survey population of this study focused on 40s and 50s' middle aged both genders living in the Seoul and national capital region, we used random sampling method. The analytical methods used in this study were frequency, mean, standard deviation, Factor Analysis, t-test, ANOVA, post-hoc estimation (Duncan test), multiple regression, To verify the reliability of each measure, Cronbach's alpha coefficient was used.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.123-138
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• 2012

Healthcare and wellness industries have become more promising as the interests on healthy living increase. Not only the medical care oriented services for the patients done by medical centers but also the psychological and emotional healthgiving services for the people who are normal have been being stressed. The psychological and emotional healthgiving services should be executed in an agile and timely manner to maximize its effects. This paper aims to propose an emotion healing service spaces which are able to provide the normal people with psychological care services. To achieve the goals, we invented the tripot approach : the ubiquitous computing technology for context-aware and intelligent estimation of psychological index, LED technology to implement emotional atmosphere and wellness healthcare technology. The proposed architecture has been implemented in an actual site.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.265-270
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• 2006

최근의 휴대전화 단말기(Cellular Phone, CP)는 IT기술을 적극적으로 접목하여 다양한 기능을 부가하고 있으나, 단순한 IT기술의 접목만으로는 CP기술 발전의 한계를 드러내고 있다. 이러한 상황에서 휴대전화 단말기에 개인용 로봇(Personal Robot)을 결합하여 로봇의 개인 서비스 기능과 엔터테인먼트 기능을 갖춘 개인로봇형 휴대전화단말기인 RCP(Robotic Cellular Phone)의 개념을 도입한 연구가 진행되고 있다. 본 논문에서는 RCP세부기술 중 하나인 $RCP^{Interaction}$에 주목한 연구로, RCP의 촉각 및 청각자극환경에서 인간이 느끼는 감성을 생체신호를 활용하여 객관적으로 감성을 평가할 수 있는 시스템에 대한 연구를 수행 하였다.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.252-257
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• 2006

감성은 인간의 생활에서 논리적 사고와 의사결정, 감정의 발생, 행동 등 모든 부분에 깊숙이 영향을 미치고 있어, 최근 감성의 개념을 도입한 공학적 제품의 도입이 활성화되어 여러 분야에 다양하게 사용 되어지고 있다. 그러나 감성을 평가함에 있어서는 단순한 해석의 의미 수준을 벗어 인간의 삶을 향상시키기 위한 제품이나 환경의 개발을 위해서는 인간의 감성을 정확하게 이해한다는 것은 체계적인 연구와 활용을 위한 선행 조건이라 할 수 있어, 생리신호등을 이용한 정량화된 감성평가 알고리즘의 개발 필요성이 있다. 특히, 최근 여러 IT기기들이 주변의 다양한 기술을 융합하여 다기능의 기기로 변모를 하고 있으며, 이러한 IT기기들에 인간의 감성을 평가할 수 있는 모듈을 부가하여 인간친화적인 기기로의 변모를 도모하고 있는 실정이다. 따라서, 본 연구에서는 측정이 용이한 소수의 생리신호만으로 간단하게 인간감성을 정량적으로 평가가 가능하며, SoC등에 간단하게 탑재할 수 있도록 시스템의 리소스를 적게 소비하는 소형 경량의 감성평가알고리즘을 개발하였다.

• Electronics and Telecommunications Trends
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• v.39 no.2
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• pp.12-23
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• 2024

The Metaverse industry is developing rapidly, and related technologies are being actively improved. Tools such as controllers, keyboards, and mouses are used to interact in the Metaverse, but they are not natural and intuitive interfaces to resemble real-world interactions. Immersive interaction in a Metaverse space requires the engagement of various senses such as vision, touch, and proprioception. Moreover, in terms of body senses, it requires a sense of body ownership and agency. In addition, eliciting cognitive and emotional empathy based on non-verbal expression, which cannot be suitably conveyed to the digital world, requires higher-level technologies than existing emotion measurement solutions. This diversity of technologies can converge to build an immersive realistic Metaverse environment. We review the latest research trends in technologies related to immersive interactions and analyze future development prospects.