• Science of Emotion and Sensibility
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• v.6 no.1
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• pp.11-16
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• 2003

In this study, we examined the evaluation of virtual Reality (VR) according to the use of motion platform. We recorded electrogastrogram, skin conductance, blood volume, skin temperature, heart rate, and the frequency of eyeblink before, during, after virtual navigation in 33 subjects. We used simulator sickness questionnaire, presence questionnaire , and self-report to evaluate cybersickness and presence in VR system. All subjects experienced VR two times in Motion platform condition and non-Motion platform condition at two-week intervals. Presence score of motion platform condition was greater than non-motion platform condition. The frequency and severity of cybersickness were significantly reduced in motion platform condition than non-motion platform condition. These results suggest that the use of motion platform that synchronizes visual presentation provides higher presence and reduces cybersickness.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• Science of Emotion and Sensibility
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• v.18 no.2
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• pp.19-30
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• 2015

The healing effect of the aroma treated fabrics with lavender and lemon aromas was investigated by assessing the autonomic nervous responses of human body. For this cause Lemon and lavender microcapsules were coated on a cotton fabric using a water-based acrylic binder, respectively. And the study created a total of four aroma treated fabrics at a concentration of 2% and 5% respectively. Electrocardiogram(ECG), skin conductance, and Blood flow, of ten participants were measured for 30 sec at a stable condition, at a stress status (working memory task), and at a stimulation status (after rubbing aroma treated fabrics). Subjective sensibilities of the aromas were also evaluated. With regard to the responses of the autonomic nervous system, in order to understand how the values gained after the normalization process would cause different physiological signals between the stable state and the aroma-stimulated state as well as between the stress state and the aroma-stimulated state, the study conducted a non-parametric test, friedman test as well and analyzed tendencies. LF/HF turned out to be significantly different to the stress state, and according to the results of the post-hoc comparison, lemon 5% presented statistically significant differences among the lavender 2%, lavender5%, lemon2%. Lemon 5% stimuli increased stress but stimuli consisting of the lavender 2%, the lavender 5% and the lemon 2% decreased stress because of a psychological rest. And the stimuli of the lavender 2%, the lavender 5%, the lemon 2% presented a healing effect in this research.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1606-1616
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• 2011

The blood pressure measuring equipment, which is being supplied and used most widely by being recognized convenience and accuracy now generally, is oscillometric blood pressure monitor. However, a change in blood pressure is basically influenced by diverse elements such as each individual's physiological status and physical condition. Thus, the measurement of blood pressure, which used single element called oscillation in blood pressure of being conveyed to cuff, is not considered on physiological elements such as cardiovascular system status and blood vessel stiffness index, and on external elements, thereby being quite in error. Accordingly, this study detected diverse bio-signals and body informations in each individual as the measurement subject such as ECG, PPG, and Korotkoff Sound in order to enhance convenience and accuracy of measuring blood pressure in the complex measurement equipment, thereby having extracted regression method for compensation in error of oscillometric blood pressure measurement on the wrist, and having improved accuracy of measuring blood pressure. To verify a method of improving accuracy, the blood pressure value in each of SBP, DBP, MAP was acquired through 4-stage experimental procedure targeting totally 51 subjects. Prior to experiment, the subjects were divided into two groups such as the experimental group for extracting regression method and the control group for verifying regression method. Its error was analyzed by comparing the reference blood pressure value, which was obtained through the auscultatory method, and the oscillometric blood pressure value on the wrist. To reduce the detected error, the blood pressure compensation regression method was calculated through multiple linear regression analysis on elements of blood pressure, individual body information, PTT, HR, K-Sound PSD change. Verification was carried out on improving significance and accuracy by applying the regression method to the data of control group. In the experimental results, as a result of confirming error on the reference blood pressure value in SBP, DBP, and MAP, which were acquired through applying regression method, the results of $-0.47{\pm}7.45$ mmHg, $-0.23{\pm}7.13$ mmHg, $0.06{\pm}6.39$ mmHg could be obtained. This is not only the numerical value of satisfying the sphygmomanometer reference of AAMI, but also shows the lower result than the numerical value in SBP : $-2.5{\pm}12.2$ mmHg, DBP : $-7.5{\pm}8.4$ mmHg, which is the mean error in the experimental results of Brram's research for verifying accuracy of Omron RX-M, which shows relatively high accuracy among wrist sphygmomanometers. Thus, the blood pressure compensation could be confirmed to be made within significant level.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.503-512
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• 2011

Recently in HCI research, emotion recognition is one of the core processes to implement emotional intelligence. There are many studies using bio signals in order to recognize human emotions, but it has been done merely for the basic emotions and very few exists for the other emotions. The purpose of present study is to confirm the difference of autonomic nervous system (ANS) response in three emotions (boredom, pain, and surprise). There were totally 217 of participants (male 96, female 121), we presented audio-visual stimulus to induce boredom and surprise, and pressure by using the sphygmomanometer for pain. During presented emotional stimuli, we measured electrodermal activity (EDA), skin temperature (SKT), electrocardiac activity (ECG) and photoplethysmography (PPG), besides; we required them to classify their present emotion and its intensity according to the emotion assessment scale. As the results of emotional stimulus evaluation, emotional stimulus which we used was shown to mean 92.5% of relevance and 5.43 of efficiency; this inferred that each emotional stimulus caused its own emotion quite effectively. When we analyzed the results of the ANS response which had been measured, we ascertained the significant difference between the baseline and emotional state on skin conductance response, SKT, heart rate, low frequency and blood volume pulse amplitude. In addition, the ANS response caused by each emotion had significant differences among the emotions. These results can probably be able to use to extend the emotion theory and develop the algorithm in recognition of three kinds of emotions (boredom, surprise, and pain) by response measurement indicators and be used to make applications for differentiating various human emotions in computer system.