• The Journal of the Korea Contents Association
• /
• v.14 no.10
• /
• pp.463-471
• /
• 2014

In this paper, emotion classification was performed by using four ocular features extracted from near-infrared camera image. According to comparing with previous work, the proposed method used more ocular features and each feature was validated as significant one in terms of emotion classification. To minimize side effects on ocular features caused by using visual stimuli, auditory stimuli for causing two opposite emotion pairs such as "positive-negative" and "arousal-relaxation" were used. As four features for emotion classification, pupil size, pupil accommodation rate, blink frequency, and eye cloased duration were adopted which could be automatically extracted by using lab-made image processing software. At result, pupil accommodation rate and blink frequency were statistically significant features for classification arousal-relaxation. Also, eye closed duration was the most significant feature for classification positive-negative.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2002.05a
• /
• pp.107-113
• /
• 2002

We examined the psychophysiological effects of navigation in a virtual reality (VR). Subjects were exposed to the VR, and required to detect specific objects. Ten electrophysiological signals were recorded before, during, and after navigation in the VR. Six questionnaires on the VR experience were acquired from 45 healthy subjects. There were significant changes between the VR period and the pre-VR control period in several psychophysiological measurements. During the VR period, eye blink, skin conductance level, and alpha frequency of EEG were decreased but gamma wave were increased. Physiological changes associated with cybersickness included increased heart rate, eye blink, skin conductance response, and gamma wave and decreased photoplethysmogram and skin temperature. These results suggest an attentional change during VR navigation and activation of the autonomic nervous system for cybersickness. These findings would enhance our understanding for the psychophysiological changes during VR navigation and cybersickness.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.2
• /
• pp.122-129
• /
• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• Journal of Oriental Neuropsychiatry
• /
• v.20 no.1
• /
• pp.119-146
• /
• 2009

Objectives : In this study, patients with tic disorders who visited an Oriental medicine clinic were examined for their demographic characteristics, characteristics of symptoms, relation to Attention-deficit Hyperactivity Disorder(ADHD) and peculiarity according to various variables such as motor and vocal tics. Methods : After surveying 292 patients who visited an Oriental medicine clinic with tic symptoms as main complaints for 17 months, SAS 9.1, a statistical program was used for statistical analysis. Results : 1. The BMI of male tic patients was significantly higher than female ones and it was similar to or higher than the normal group. 2. Patients who are eldest children were 1.7 times higher than those who are not eldest ones. 3. The most usual case of motor tics was the eye blink and the most one of vocal tics was a dry cough. 4. There was no significant difference between male and female patients for all symptoms of motor and vocal tics, but male patients had significantly more obsessions related to tics than female ones. 5. There was no significant difference in the age of initial occurrence of Transient tic disorder(TTD), Chronic tic disoder(CTD) and Tourette's disorder(TD). 6. For the general disorder of a tic and Conners' ADHD rating scale, there was no significance in TTD, CTD and TD. 7. 66% out of the total subjects of 197 cases were found to score more than 65 points in more than 1 items among 8 items such as the time, hearing, wrong alarm, mean response time and standard deviation in the response time, etc. of the ADHD diagnosis system(ADS). 8. The eye blink among motor tics was shown mainly by patients under 10 years old and the frown, movement of the head, shrug and movement of the arms were shown mainly by 11-19 years old patients. Conclusions : For the number, frequency, seriousness and inconvenience in life of tics, TD showed a significantly higher result than TTD and CTD.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.3
• /
• pp.580-585
• /
• 2009

Real Time Locating Systems (RTLS) are used to track and identify the location of objects in real time using simple, inexpensive tags attached to or embedded in objects and readers that receive the wireless signals from these tags to determine their locations. A tag is powered an internal source such as a battery. The blink frequency of a tag affects the energy efficiency and the locating accuracy of RTLS. The mobility of a tag also affects the locating accuracy. In this paper, we introduce a RTLS tag design which improves the locating accuracy and the power efficiency. We propose an adaptive transmission-rate control algorithm using a motion sensor. By analyzing the signal pattern of the motion sensor, we can build a model to estimate the speed of the motion. Using this model, our algorithm can achieve better locating accuracy and lower power consumption than those of the conventional method. In our experiments, the number of transmission reduced as 40%, keeping similar locating accuracy.

• Advances in robotics research
• /
• v.2 no.2
• /
• pp.141-149
• /
• 2018

In image processing and robotic applications, two-dimensional (2D) black and white patterned planar markers are widely used. However, these markers are not detectable in low visibility environment and they are not changeable. This research proposes an active and adaptive marker node, which displays 2D marker patterns using light emitting diode (LED) arrays for easier recognition in the foggy or turbid underwater environments. Because each node is made to blink at a different frequency, active LED marker nodes were distinguishable from each other from a long distance without increasing the size of the marker. We expect that the proposed system can be used in various harsh conditions where the conventional marker systems are not applicable because of low visibility issues. The proposed system is still compatible with the conventional marker as the displayed patterns are identical.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.693-696
• /
• 2019

IT 기술은 끊임없는 발전을 거듭하고 있으며 현 인류는 기계와 더불어 살고 있다. Desktop, 스마트폰, 노트북 및 태블릿PC는 물론이고, 스마트 워치와 같은 '웨어러블 디바이스(Wearable Device)'의 등장으로 기계 속 세상에 그들과 함께 살고 있다하여도 과언이 아니다. 단연 잦은 기기 사용으로 인해 가장 영향을 크게 받는 인간의 신체 부위는 '눈'이다. 휴대용 기기(Portable Device)는 휴대에 용이해야 한다는 특징 때문에 그 크기가 점차 작아지고 있다. 따라서 작은 기기에 부착된 화면 역시 크기가 감소하였다. 장시간 작은 화면을 집중하여 보게 되면 눈의 피로가 금방 쌓이게 된다. 이로 인해 안구 건조 증 및 시력 저하 발생률이 증가하게 되는데, 영상처리 기술을 이용하여 안구의 깜박임을 감지하고 일정 수치 이하로 깜박임 횟수가 미달될 경우에 안구 운동을 권장하는 프로그램을 개발 하였다.

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.274-284
• /
• 2021

Customized services to a sleep induction for better sleepcare are more effective because of different satisfaction levels to users. The EOG data measured at the frontal lobe when a person blinks his eyes can be used as biometric data because it has different values for each person. The accuracy of measurement is degraded by a noise source, such as toss and turn. Therefore, it is necessary to analyze the noisy data and remove them from normal EOG by filtering. There are low-pass filtering and high-pass filtering as filtering using a frequency band. However, since filtering within a frequency band range is also required for more effective performance, we propose a machine learning model for the filtering of EOG data in this paper as the second filtering method. In addition, optimal values of parameters such as the depth of the hidden layer, the number of nodes of the hidden layer, the activation function, and the dropout were found through experiments, to improve the performance of the machine learning filtering model, and the filtering performance of 95.7% was obtained. Eventually, it is expected that it can be used for effective user identification services by using filtering model for EOG data.

• Journal of Korea Multimedia Society
• /
• v.20 no.10
• /
• pp.1646-1654
• /
• 2017

Pupil size variation can not be controlled intentionally by the user and includes various features such as the blinking frequency and the duration of a blink, so it is suitable for understanding the user's emotional state. In addition, an ocular feature based emotion classification method should be studied for virtual and augmented reality, which is expected to be applied to various fields. In this paper, we propose a novel emotion classification based on CNN with pupil size variation signals which include not only various ocular feature information but also time information. As a result, compared to previous studies using the same database, the proposed method showed improved results of 5.99% and 12.98% respectively from arousal and valence emotion classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.754-755
• /
• 2015

Computer Vision Syndrome(CVS) is a temporary condition resulting from focusing the eyes on a computer display for protracted, uninterrupted periods of time. To prevent CVS, you have to blink your eyes frequently, also have to keep distances from monitor. In this paper, real-time notification system for preventing CVS by checking user's distance between eyes and monitor and user's frequency of nictation in real time through monitor webcam is described.