• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.171-175
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• 2004

A tactile stimulator array using stacked PZT is fabricated and evaluated in this paper. The purpose of this research is the development of a tactile stimulator to represent the obstacle information for the visually disabled. As a first step of this research, we investigate the physiological characteristics of tactile stimuli and design a tactile stimulator based on the investigated results. Also we evaluated a fabricated tactile stimulator. The prototype of tactile stimulator which has 2x2 tactor elements with 3mm spacing is fabricated using stacked PZT actuator. In order to evaluate the characteristics of this tactile stimulator, physiological experiments are carried out. In the experiment, the threshold of tactile stimulus intensity within a frequency range of 5-500Hz and at various stimulus amplitudes are investigated. According to the obtained experimental result, the input signal of tactile stimulator for the transfer of obstacle information is determined. Also physiological experiments of multi-stimuli recognition such as shift and rotation are carried out.

• Journal of Sensor Science and Technology
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• v.18 no.5
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• pp.337-342
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• 2009

This paper describes a robust mobile u-healthcare system with multiple physiological signs measurement capability in real time with integration of WSN(wireless sensor network) technology and CDMA(code division multiple access) network. A cellular phone receives health data in WSN and performs local physiological signs analysis at a phone processor, and then transmits abnormal data to server for further detail or precise health signal evaluation by a medical doctor over a CDMA network. Physiological signs of the patients are continuously monitored, processed and analyzed locally at cellular phone process to produce useful medical information for diagnosis and tracking purposes. By local simple analysis in cellular phone processor we can save the data transmission cost in CDMA network. By using the developed integrate ubiquitous healthcare service architecture, patients can realize self-health checking so that the prevention actions can be taken earlier. Appropriate self-monitoring and self-management can cure disease and relieve pain especially for patients who suffer from chronic diseases that need long term observation.

• Science of Emotion and Sensibility
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• v.23 no.3
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• pp.3-10
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• 2020

This study is aimed at investigating drivers' reactions to yellow signal dilemma situations as a result of the existing signal system, and developing a new signal system. A driver-centered coping model was developed through bio-signal analysis. The driver's physiological response in the existing signal system was observed, and the signal system was developed by applying intersection road driving conditions using a car graphic simulator. Participants were classified into a control group (existing signal system) and an experimental group for a new yellow signal system (new signal system). Based on the results, the emergence of parasympathetic nerves was higher in the experimental group than in the control group, where a statistically significant difference was observed (p < 0.05). The newly developed signal system appeared to cause tension among drivers; however, the sympathetic to parasympathetic nerve ratio was 6: 4, which could be interpreted as an ideal balance. We conclude that drivers can drive more stably if the coping signal system developed in this study is applied to the traffic system.

• Investigative Magnetic Resonance Imaging
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• v.18 no.3
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• pp.244-252
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• 2014

Purpose : We proposed a multi-physiological signals based real-time intelligent triggering system(MITS) for Cardiac MRI. Induced noise of the system was analyzed. Materials and Methods: MITS makes cardiac MR imaging sequence synchronize to the cardiac motion using ECG, respiratory signal and second order derivative of $SPO_2$signal. Abnormal peaks due to arrhythmia or subject's motion are rejected using the average R-R intervals and R-peak values. Induced eddy currents by gradients switching in cardiac MR imaging are analyzed. The induced eddy currents were removed by hardware and software filters. Results: Cardiac MR images that synchronized to the cardiac and respiratory motion are acquired using MITS successfully without artifacts caused by induced eddy currents of gradient switching or subject's motion or arrhythmia. We showed that the second order derivative of the $SPO_2$ signal can be used as a complement to the ECG signals. Conclusion: The proposed system performs cardiac and respiratory gating with multi-physiological signals in real time. During the cardiac gating, induced noise caused by eddy currents is removed. False triggers due to subject's motion or arrhythmia are rejected. The cardiac MR imaging with free breathing is obtained using MITS.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.229-235
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• 2013

In this paper, we investigate several important issues on the implementation of a totally implantable microsystem for brain-machine interface that has been attracting a lot of attention recently. So far most of the scientific research has been focused on the high performance, low power electronics or systems such as neural signal amplifiers and wireless signal transmitters, but the real application of the implantable microsystem is affected significantly by a number of factors, ranging from design of the encapsulation structure to physiological and anatomical characteristics of the brain. In this work, we discuss on the thermal effect of the system, the detecting volume of the neural probes, wireless data transmission and power delivery, and physiological and anatomical factors that are critically important for the actual implementation of a totally brain implantable neural interface microsystem.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.81-103
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• 2005

The Neuron structure in a nervous system consists of inhibitory neurons and excitory neurons. Both neurons are activated by agonistic neurons and inactivated by antagonist neurons. In this paper, we proposed a physiological fuzzy neural network by analyzing the physiological neuron structure in the nervous system. The proposed structure selectively activates the neurons which go through a state of excitement caused by agonistic neurons and also transmit the signal of these neurons to the output layers. The proposed physiological fuzzy neural networks based on the nervous system consists of a input player, and the hidden layer which classifies features of learning data, and output layer. The proposed fuzzy neural network is applied to recognize bronchial squamous cell carcinoma images and car plate images. The result of the experiments shows that the learning time, the convergence, and the recognition rate of the proposed physiological fuzzy neural networks outperform the conventional neural networks.

• Journal of Sensor Science and Technology
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• v.5 no.1
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• pp.43-50
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• 1996

This paper presents a manufacture of the multiple subjects biotelemetry system using custom CMOS IC fabricated $1.5{\mu}m$ n-well process technology. The implantable circuits of the system except sensor interface circuits including FM transmitter are fabricated on a single chip with the sire of $4{\times}4mm^{2}$. It is possible to assemble the implantable system in a hybrid package as small as $3{\times}3{\times}2.5cm$ by using this chip, It's main function is to enable continuous measurement simultaneously up to 7-channel physiological signals from the selected one among 8 subjects. Another features of this system are to enable continuous measurement of physiological signals, and to accomplish ON/OFF switching of an implanted battery by subject selection signal with command signal from the external circuit. If this system is coupled with another appropriate sensors in medical field, various physiological parameters such as pressure, pH and temperature are to be measured effectively in the near future.

• Journal of the Ergonomics Society of Korea
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• v.24 no.1
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• pp.9-17
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• 2005

81.1% of traffic accidents is attributed to the drivers. In this regard, D/E model is a practical and effective method in terms of the cost and time in evaluating the road hazardousness. To examine the validity of the threshold values by the levels of demand We selected 10 subjects and collected their physiological signals while they were driving on Honam Highway (Jeonju ${\leftrighttarro}$ Hoideog section). Based on the collected data, the hazardous road condition was evaluated using the new threshold values of the effort level determined by cluster analysis. In applying the D/E model, a decision method based on the demand level was suggested, using a traffic accident prediction model. Additionally, the limit value of the effort level was determined using the drivers' physiological signal data collected at the highway. A comparison analysis of the two D/E models revealed no significant difference: The existing method and the clustering method determined 9 and 7 hazardous road zones, respectively, while actual traffic accidents were reported in 6 and 4 zones, respectively among the predicted road hazardous zones. However, the latter method suggested a more scientific and rational basis in determining the limit value of the Effort level. In conclusion, although D/E model has a great merit as a pioneering method to reflect human factors in evaluating the road hazardousness, it is believed that this method could be improved by a more dynamic method that considers the traffic conditions and the individual physiological signal of the drivers simultaneously in determining a better limit.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1322-1325
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• 1987

In this study, the excitation pattern, which can be sensated by a man having hearing loss due to the damage of inner ear, is extracted, and the procedure of the auditory speech signal processing is simulated with the computer. Therefore, the excitation pattern is extracted by the neural tuning model satisfying the physiological characteristic of the inner ear and by the infor.ation extracted from speech signal. The firing pattern is also extracted by inputting this excitation pattern to the auditory neural model. With this extracted firing pattern, the possibility that the patient can sensate the speech signal is studied by the computer simulation.