• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.251-256
• /
• 2008

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.2
• /
• pp.449-454
• /
• 2020

This paper proposes a machine learning-based emotion analysis system that detects a user's depression through their SNS posts. We first made a list of keywords related to depression in Korean, then used these to create a training data by crawling Twitter data - 1,297 positive and 1,032 negative tweets in total. Lastly, to identify the best machine learning model for text-based depression detection purposes, we compared RNN, LSTM, and GRU in terms of performance. Our experiment results verified that the GRU model had the accuracy of 92.2%, which is 2~4% higher than other models. We expect that the finding of this paper can be used to prevent depression by analyzing the users' SNS posts.

• Science of Emotion and Sensibility
• /
• v.15 no.1
• /
• pp.149-160
• /
• 2012

The relative effects of individual differences in cultural orientation (individualism vs. collectivism) and locus of control (LOC: internal vs. external control beliefs) combination types on global/local processing were compared by manipulating the compound stimulus types (arrows or letters), and the stimulus-stimulus congruence. The results can be summarized as followings. First, consistent with previous research on global/local processing of the compound stimuli, reaction time (RT) for global stimuli than for local stimuli, and that in the stimulus-stimulus congruent condition than in the stimulus-stimulus incongruent condition was faster. Second, faster RT was found in the compound arrows condition than in the compound letters. Third, individual difference in LOC, rather than that in the cultural orientations, appeared to be related to global precedence effect, when the compound letters were presented. These results indicated that the individual's LOC rather than cultural orientation can increase the size of the global precedence effect, which might be involved in the stage of cognitive analysis than that of feature detection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.3099-3120
• /
• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Science of Emotion and Sensibility
• /
• v.13 no.3
• /
• pp.441-448
• /
• 2010

In the aging society, the walking assist robot is a necessary device for being able to help the older and the lower limb disabled people to walk. In order to produce a convenient robot for the older and the lower limb disabled, it is needed for the research to detect the implicit walking intention and to control robot by a user's intention. This study is a previous study to develop the detection model of the walking intention and analyze the user's walking intention while a person is walking with Lofstrand crutches, by the combination of FSR and tilt signals. The FSR sensors attached user's the palm and the soles of foot are sensing force/pressure signals from these areas and are used for detecting the walking intention and states. The tilt sensor acquires roll and pitch signal from area of vertebrae lumbales and reflects the pose of the upper limb. We can recognize the user's walking intention such as 'start walking', 'start of right or left foot forward', and 'stop walking' by the combination of FSR and tilt signals can recognize.

• Science of Emotion and Sensibility
• /
• v.14 no.3
• /
• pp.459-466
• /
• 2011

Sleep diseases such as snoring and sleep apnea are physically, mentally harmful and results serious health problems. Snoring, known as breathing noise, is caused by coupled oscillation of the airway when the air passes through the trachea, and sleep apnea is caused by upper airway blockage. In order to solve these problems, many attempts have been made to detect the snoring during sleep and alleviate it. In this study, a new sensing system and analysis algorithm were developed in order to detect snoring sounds correctly under various sleep environments. Two polyvinylidene fluoride (PVDF) vibration sensors were used inside the pillow. The first PVDF sensor detects vibration transmitted through skull caused by snoring. And the second PVDF sensor detects both snoring sounds and ambient noises. The signals of two sensors were acquired through the designed analog circuits, and analyzed for snoring detection. Ten volunteers were participated for the experiment under five different conditions. Data from two PVDF sensors were processed by the established analysis algorithm, and snoring sounds were compared to noises. The results indicated that the energy of snoring is 70% bigger than that of ambient noise, which proves effectiveness of sensing system and analysis algorithm. Further study would be continued for more wide clinical studies with various environment noises. Based on this study, development of anti-snore pillow and sleep monitoring system for comfort sleep could be developed.

• Science of Emotion and Sensibility
• /
• v.5 no.2
• /
• pp.29-34
• /
• 2002

Our previous studies investigated that narrow field of view (FOV : 50˚) and slow navigation speed decreased the frequency of occurrence and severity of cybersickness during immersion in the virtual reality (VR). It would cause a significant reduction of cybersickness if it were provided cybersickness alleviating virtual environment (CAVE) using biofeedback method whenever subject underwent physiological agitation. For verifying the hypothesis, we constructed a real-time cybersickness detection and feedback system with artificial neural network whose inputs are electrophysiological parameters of blood pulse volume, skin conductance, eye blink, skin temperature, heart period, and EEG. The system temporary provided narrow FOV and decreased speed of navigation as feedback outputs whenever physiological measures signal the occurrence of cybersickness. We examined the frequency and severity of cybersickness from simulator sickness questionnaires and self-report in 36 subjects. All subjects experienced VR two times in CAVE and non-CAVE condition at one-month intervals. The frequency and severity of cybersickness were significantly reduced in CAVE than non-CAVE condition. Virtual environment of narrow FOV and slow navigation provided by electrophysiological features based artificial neural network caused a significant reduction of cybersickness symptoms. These results showed that efficiency of a cybersickness detection system we developed was relatively high and subjects expressed more comfortable in the virtual navigation environment.

• Science of Emotion and Sensibility
• /
• v.20 no.1
• /
• pp.115-132
• /
• 2017

Differences in reaction time and accuracy were compared among driver groups of middle-, old-, and stroke old-age drivers using various reaction time tasks including simple reaction task, 2-choice task, 4-choice task with different stimuli eccentricity, search task, and moving target detection task. The results can be summarized as followings. First, although overall reaction time tended to be slowed with age and stroke, stroke old drivers showed significantly slower reaction time than the other driver groups when the stimuli were presented in a large eccentricity. Second, differences in reaction time for 2-choice task and moving target detection task seemed to be determined mainly by participants' simple reaction time. Third, the search task which required temporary retention of previously presented stimuli was found to be more sensitive in discriminating difference in reaction time between middle-age drivers and old-age drivers (including stroke old drivers). Fourth, reaction accuracy of old (and stroke old) drivers decreased when more stimuli alternatives were presented and temporary retention for stimuli was required. Altogether, memory demand in reaction time task can be sensitive to evaluate performance for different age groups, whereas size of useful field of view for brain stroke.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.159-165
• /
• 2017

We were investigated the content of 7 preservatives for sheet mask samples(n=42) sold in markets of Daejeon metropolitan city in 2016. &3.3%(n=35) of all samples contained at least one of preservatives. In samples of 30.95(n=14) and 2.39%(n=1) was detected with 2 and 3 preservatives. Phenoxyethaol(PE), methylparaben(MP), chlorphenesin(CP) and benzyl alcohol(BA) showed detection rate of 76.19(n=32), 16.67(n=9), 21.43(n=7) and 2.38%(n=1), respectively. Also The content of detected preservative showed range of 0.06~0.71, 0.18~0.35, 0.06~0.71 and 0.32% and was within the maximum allowed amount established by Korean FDA. However ethylparaben(EP), propylparaben(PP) and benzylparaben(BP) in all samples was not detected. These results can be useful for sharing in emotion-fusioned information and supplying right to know of user.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.179-188
• /
• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.