• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.44-49
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• 2004

The aim of this study is to compare the methods in analyzing bio-signals representing measure driver's psychophysiological staus. This study has considered three approaches: first, the deterministic approach calculating the mean and standard deviation of bio-signal, second, probabilistic approach converting driver's bio-signal values to probability density function and identifying individual state relative to overall distribution, and third, diagnostic approach identifying the pattern change of signal over certain period of time. For evaluation of analysis methods, driver's bio-signal was collected under various road conditions, and three analysis approaches were applied respectively. In result, the deterministic approach was found to be simple to use, but generated a large variability of bio-signal. The probabilistic approach provide a relative status of individual driver among overall population, but too much affected by temporal variability of individual driver. The diagnostic approach seemed to reasonably find driver's psychophysiological change over certain period of time, but still needs to develop quantification method of the bio-signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4C
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• pp.283-289
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• 2012

Web-based physiological signal monitoring system can provide appropriate healthcare services to transmit bio-signal processing, analysis of bulk in medical centers. Therefore, we constructed a design of system to analyze effect of cardiac associated auricular acupuncture reflexology based on physiological signals. System to analyze effect cardiac associated auricular acupuncture reflexology, which carried out analysis and measurement of bio-signal to apply cardiac-related biometrics input in biometric image and voice signal. In addition, we also confirmed through statistical analysis actual home healthcare system to performance evaluation of system on subjects 20.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.267-268
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• 2002

We developed nausea, caused by disorder of autonomic nervous system, detection system using bio-signal analysis and artificial neural network in virtual reality enironment. We used 16 bio-signals, 9 EEGs, EOG, ECG, SKT, PPG, GSR, RSP, EGC, which has own analysis methods. We estimated nausea level by artificial neural network.

• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.1
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• pp.52-55
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• 2002

Flow cytometric techniques were used to investigate cell size, protein content and cell cycle behavior of recombinant Saccharomyces cerevisiae strains producing human lysozyme (HLZ). Two different signal sequences, the native yeast $MF\alpha1$ signal sequence and the rat $\alpha-amylase$ signal sequence, were used for secretion of HLZ. The strain containing the rat $\alpha-amylase$ signal sequence showed a higher level of internal lysozyme and lower specific growth rates. Flow cytometric analysis of the total protein content and cell size showed the strain harboring the native yeast signal sequence had a higher total protein content than the strain containing the rat $\alpha-amylase$ signal sequence. Cell cycle analysis indicated that the two lysozyme producing recombinant strains had an increased number of cells in the $G_2+M$ phase of the yeast cell cycle compared with the host strain SEY2102.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.121-131
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• 1999

There have been many questionnaires, catecholeamins analysis and bio-signal analysis to analyze human stress condition through out the years, and especially researches in bio-signal analysis have been actively increasing. The purpose of our research is Quantitative analysis of stress with synthesis of bio-signals. The stress status was estimated using the bio-signals and fuzzy theory which combines these signals and physiological knowledge. Stress was estimated by a 'coin-stacking' experiment with two type-relax and stress status. To do the experiment EMG, respiration, periphery temperature, heart rate and skin conductances were used to evaluate human stress stages. The system was tested to 10 healthy persons and achieved a template of a stress progress and stress variations were classified to 4 steps by continuous or rising status of stress progress.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.261-268
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• 1998

Electrogastrography(EGG), the cutaneous recording of the myoelectrical activity of the stomach using surface electrodes, is a non-invasive technique to detect gastric motility disorder, We developed a detection and analysis system for the EGG signal, which consists of hardware(bio-amplifier, filter) and softwere(user interface, analysis algorithm, patient database). The EGG signal was amplified and filtered by 3 channel bio-amplifiers, and simultaneously digitized and stored on IBM PC with a sampling frequency of 16 Hz. The stored EGG signal was analyzed using developed algorithm to extract clinically useful information from the signal. The developed system has tested through animal experiments, and is under clinical evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.65-67
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• 2017

Due to the development of mobile computing and wearable technology, various wearable devices for measuring bio-signals in everyday life have been developed and popularized, and healthcare services utilizing bio-signals are attracting attention. In recent years, healthcare services have been developed and studied using various bio-signal analysis tools. Most bio-signal analysis studies utilize Matlab. However, in order to apply the algorithm developed in Matlab to the system, it is necessary to convert the source. We want to provide a smart interface that can skip source conversion. In this paper, we develop an interface to run the source file itself in the system by omitting the conversion technique for applying the algorithm developed in Matlab to the system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.238-240
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• 2018

Recently, healthcare services have been developed and studied using various bio-signal analysis tools. Most bio-signal analysis studies utilize Matlab and R Programming. However, in order to apply the algorithm developed by Matlab and R Programming to the system, it is necessary to convert the source code. This paper proposes a smart interface that can skip source code conversion.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.935-939
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• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.299-306
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• 2013

The purpose of this research is to analyze the questionnaire of oriental medicine and bio-signal data and to identify the correlation between symptoms of oriental medicine and bio-signal in order to develop the customization model for individual patients. First, this research analyzes the questionnaire data using the developed questionnaire for oriental medicine symptoms and selects the optimized questionnaires for identifying the symptoms. Using data mining technologies with the selected questionnaires, this research divides the normal group and the group with symptoms. Finally, this research identifies the correlation between the questionnaire for oriental medicine symptoms and electrogastrogram signal.