• Journal of Korean Academy of Fundamentals of Nursing
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• v.13 no.2
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• pp.275-284
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• 2006

Purpose: The purpose of this study was to analyze tile trends in physiological variables in CAT research in nursing in Korea. Method: Of studies published in Korea between January, 2000 and December, 2005, 227 studies were analyzed according to the criteria of type, theme, and patterns in physiological variables related to CAT. Results: There were 72 articles on CAT research in which physiological variables were examined. The most frequently researched type of CAT was massage and in particular, foot massage. The most frequently used physiological variables in CAT research were blood pressure, pulse, and body temperature. Patients with high blood pressure were the most frequent subjects for CAT research. As to the effect of physiological variable by CAT type, foot massage showed a decline in blood pressure in all six research studies involving patients with high blood pressure. Conclusion: There is a need to describe accurately the mechanism by which CAT affects physiological variables. There is also a need for repetitive analysis to verify the effect, and meta-analysis for the effect on physiological variables according to type of CAT.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.13-18
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• 2008

In these days, wearable and mobile physiological sensing devices have been studied according to the increasing interest on the healthy and wellbeing life. However, these sensing devices display just the sensing results, such as heart rate, skin temperature, and its daily records. In this work, we propose the novel type of mobile physiological sensing device which deliver the user comfortable grabbing feeling. In addition, we indicate the personalized physiological signal analysis result which be concluded by the different analysis results according to the person to person. In order to verify this sensing device, we collect the data set from 4 different users during a week and measure the physiological signal such as heart rate, hand temperature, and skin conductance. And we observe the result how the analysis results shows the difference between the users. We expect that this work can be applied in the various health care applications in the near future.

• ETRI Journal
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• v.37 no.6
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• pp.1231-1241
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• 2015

In this study, we investigate the relationship between emotions and the physiological responses, with emotion recognition, using the proposed fuzzy information granulation-based neural network (FIGNN) for boredom, pain, and surprise emotions. For an analysis of the physiological responses, three emotions are induced through emotional stimuli, and the physiological signals are obtained from the evoked emotions. To recognize the emotions, we design an FIGNN recognizer and deal with the feature selection through an analysis of the physiological signals. The proposed method is accomplished in premise, consequence, and aggregation design phases. The premise phase takes information granulation using fuzzy c-means clustering, the consequence phase adopts a polynomial function, and the aggregation phase resorts to a general fuzzy inference. Experiments show that a suitable methodology and a substantial reduction of the feature space can be accomplished, and that the proposed FIGNN has a high recognition accuracy for the three emotions using physiological signals.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.173-177
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• 2009

With the advent of light-weight daily physiological signal monitoring sensors, intelligent inference and analysis method for physiological signal monitoring application, commercialized products and services are released. However, practical constraints still remain for daily physiological signal monitoring. Most devices provide rough health check function and analyze with randomly sampled measurements. In this work, we propose the probabilistic modeling of physiological signal analysis. This model represent the relationship between previous user measurement (history), other group`s type, model and current observation. From the experiment, we found that the personalized analysis with long term regular data shows reliable result and reduces the analyzing errors. In addition, participants agree that the personalized analysis shows reliable and adaptive information than other standard analysis method.

• International Journal of Contents
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• v.10 no.2
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• pp.47-58
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• 2014

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Korean Journal of Applied Biomechanics
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• v.20 no.3
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• pp.303-310
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• 2010

The purpose of this study was (a)to compare electromyographic (EMG) activities and physiological variables on the development of fatigue induced by ergometer test, (b)to determine investigate the differences in the stage of fatigue between the electromyographic characteristics and physiological variables. Nine male university students who have no musculoskeletal disorder were recruited as the subjects. The electromyographic characteristics(peak IEMG, average IEMG, median frequency, mean edge frequency) and physiological variables(HR, RPE, blood lactate) were determined for each stage(15, 30, 45, 60 minutes, all out). For each dependent variable, one-way analysis of variance(ANOVA) with repeated measures and correlation analysis were performed to test if significant difference existed(p<.05). The results showed that peak IEMG, average IEMG from low extremity and physiological variables were significantly increased during the all-out stage. EMG parameters in VL, VM show significantly correlation with physiological variables during whole stages. This indicated that IEMG values may be proper parameters to determine muscle fatigue rather than physiological variables.

• Journal of Chest Surgery
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• v.10 no.2
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• pp.274-280
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• 1977

As a major cause for postoperative hypoxia, the importance of increased physiological shunting is increasingly emphasized. This study is a review and analysis of postoperative physiological shunting following open heart surgery with the aid of extracorporeal circulation. Sixteen patients were selected from among 21 patients who underwent elective open heart surgery at the Department of Thoracic and Cardiovascular Surgery, Kyungpook National University, School of Medicine, from December, 1975 to September, 1977. The results were as follows: 1. The degree of postoperative physiological shunt was progressively increased from 18.8% mean value one hour after surgery to 22.7% mean value, reaching a peak on the second postoperative days. 2. For up to one week, large physiological shunt[15%] was persisted in one patient. 3. Comparing long[more than 90 minutes] with short[less than 90 minutes] perfusion time group using pump oxygenator, it was found that the physiological shunt increased about 3% in the long as compared with the short perfusion time group. 4. The mean blood pressure was 70-80 mmHg without a remarkable causal relationship between physiological shunt and mean blood pressure. 5. On elevated $PaO_2$[>200 torr], the physiological shunt was decreased less than 20% of cardiac output, but on diminished $PaO_2$[102 torr] after two days, it was 22.7% of cardiac output. From above results, a contrary causal relationship between $PaO_2$ and physiological shunt was obtained. Co Reviewing chest X-rays postperfusion, it was demonstrated that no remarkable causal relationship between roentgen-ray evidence and physiological shunt could be obtained.

• Science of Emotion and Sensibility
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• v.4 no.1
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• pp.23-31
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• 2001

Psychological and physiological effects from simulator sickness could be an important bias factor for sensibility evaluation. The present experiment investigated the effects of simulator sickness on sensibility evaluation in the controlled condition of driving a car for 60 minutes on a constant speed (60km/h) in graphic simulator. The simulator sickness was measured and analysed for every five minutes using their subjective evaluation and physiological signals. Results of the subjective evaluation showed that there was significant difference between rest and driving condition at 10 minutes from the start of driving, and the level of difference was increased linearly with time. The analysis on central and autonomic nervous systems showed the significant difference between rest and driving conditions after 5 minutes from the start of the driving on the parameters $\alpha$/total and $\beta$/total, and increased level of sympathetic nervous system. But there was no significant difference between different time conditions. The results indicates that physiological changes from simulator sickness can be a bias factor in objective evaluation of human sensibility which also, uses physiological signals. That is, the changes on the parameter $\alpha$/total and $\beta$/total, and on activation level of sympathetic nervous system from simulator sickness can be a bias factor for evaluation of the level of pleasantness and tension. Therefore the effort on improving the analysis by minimizing or eliminating the bias factors should be done for better and accurate sensibility evaluation in simulator environments.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.27-35
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• 2004

This study has investigated a psychological status of optimal and pleasant driving condition by measuring various physiological signals using SCR(skin conductance response), PPG(peripheral plethysmograph), SKT(skin temperature) and HR(heart rate). The physiological response was measured during various simulated driving conditions. We developed a hardware and algorithm to measure and analyze the physiological response. The physiological signals has reflected the level of driver's tension or relaxation as well as the heart rate. The emotional responses of drivers were also measured and analyzed in this experiment. The result of the study can be used to design a system to enhance the driver's emotional satisfaction as well as to monitor the driver's safety and health condition.