• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.96-98
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• 2016

Recently, big data technology develop due to the rapid data generation. Thus big data analysis tools for analyzing big data has been developed. Typical big data tools are the R program, Hive, Tajo and more. But data analysis based on Matlab is still common used. And it is still used in big data analysis. In this paper, it research into big data analysis system based on the Matlab for analyzing vital signals.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.206-211
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• 1998

본 논문에서는 복잡한 생체 전기신호 및 자연계에서 획득될 수 있는 각종의 비선형 신호들을 효율 좋게 분석할 수 있는 시뮬레이터의 개발에 대하여 지금까지 연구된 내용에 대하여 기술한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.239-244
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• 2001

5∼7세 어린이 12명을 대상으로 생체 신호를 수집하여 기존의 생체 신호 처리 방법으로 감성을 추출하고 그 결과의 문제점을 보인다. 문제점들을 해결하기 위하여 Mental Stress를 측정하는 RRV, RPIAD 알고리듬을 적용하고 감성 완구에 적용 가능성 및 방법을 보인다. 이를 상품화할 수 있는 Affective Wearable 하드웨어를 설계하며 제안된 알고리즘과 하드웨어를 시험하고 검증하기 위한 Test Bed를 구성한다.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.270-273
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• 1995

중앙 집중식 태아 전자감시 시스템은 태아의 안녕을 판정하는 중요한 평가방법이다. 그러나, 태아감시 시스템은 입력 신호의 장기간의 변동 추이를 검사해야 하며, 따라서 해석하는 사람의 집중력이 쉽게 저하될 수 있다. 컴퓨터를 이용한 태아 감시장치 신호의 자동해석은 분만장의 의사 및 간호사의 업무를 한층 부담없고 손쉽게 할 수 있다. 본 연구에서는 중앙 집중식 태아감시 시스템에서 간단하고 효율적으로 적용할 수 있는 신호 해석 알고리듬을 제시하고, 그에 따른 고속 프로그램을 구현하여 실제 시스템에 적용해 봄으로써 신호 해석 알고리듬의 실효성을 검토해 보고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.113-123
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• 2020

Obstructive sleep apnea (OSA) among sleep disorders is one of relatively common diseases. Patients can be checked for the disease through sleep polysomnography. However, as far as he diagnosis of OSA using polysomnography (PSG) is concerned, many practical problems such as an increasing number of patients, expensive testing cost, discomfort during examination, and the limited number of people for testing have been pointed out. Accordingly, for the purpose of substituting PSG researchers have been actively conducting studies on OSA diagnosis based on machine learning using bio signals. In this regard, we review a rich body of existing OSA diagnosis studies applying machine learning techniques based on bio-signal data. As a result, this paper presents a novel taxonomy of the reviewed studies and provides their comprehensive comparative analysis results. Also, we reveal various limitations of the studies using the bio signals and suggest several improvements about utilization of the used machine learning methods. Finally, this paper presents future research topics related to the application of machine learning techniques using bio signals.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.461-467
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• 2002

Signals reduced from the brain had been considered as a noise that is caused by the stochastic process until 1980. The recent non-linear dynamic theory researches, however, reported that these signals are meaningful and deterministic chaos signals in which they show how the brain deals with various information Since this report, a wide range of researches has been carried out and still in progress. Thus, by using the correlational dimension, one of the non-linear analytical methods, the characteristics of the brain signals can be analyzed. In this thesis, the scent of lavender, which stimulates the olfactory sense, is introduced to measure EEG with the International 10-20 electrode system on 16 channels, and to analyze the interrelationship between the original signals before the stimulation and the changed signals after the stimulation. Finally, the effect of the scent stimulation to the brain is analyzed. The purpose of this thesis is to apply these analyzed results to the computerized mapping of the brain signals and possible ways of specifying the source of the brain signals through various medical applications.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.461-467
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• 2002

Signals produced from the brain had been considered as a noise that is caused by the stochastic process until 1980. The recent non-linear dynamic theory researches, however, reported that these signals are meaningful and deterministic chaos signals in which they show how the brain deals with various information Since this report a wide range of researches has been carried out and still in progress. Thus, by using the correlational dimension, one of the non-linear analytical methods, the characteristics of the brain signals can be analyzed. In this thesis, the scent of lavender, which stimulates the olfactory sense, is introduced to measure EEG with the International 10-20 electrode system on 16 channels, and to analyze the interrelationship between the original signals before the stimulation and the changed signals after the stimulation. Finally, the effect of the scent stimulation to the brain is analyzed. The purpose of this thesis is to apply these analyzed results to the computerized mapping of the brain signals and possible ways of specifying the source of the brain signals through various medical applications.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.581-587
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• 2004

Conventional power spectrum methods based on FFT, AR method are not appropriate for analyzing biomedical signals whose spectral characteristics change rapidly. On the other hand, time-frequency analysis has more desirable characteristics of a time-varying spectrum. In this study, we investigated the spectral components of heart rate variability(HRV) in time-frequency domain using time frequency analysis methods. In the various time-frequency kernels functions, we studied the suitable kernels for the analysis of HRV using synthetic HRV signals. First, we evaluated the time/frequency resolution and cross term reduction of various kernel functions. Then, from the instantaneous frequency, obtained from time-frequency distribution, the method extracting frequency components of HRV was proposed. Subjects were 17 healthy young men. A coin-stacking task was used to induce mental stress. For each subjects, the experiment time was 3 minutes. Electrocardiogram, measured during the experiment, was analyzed after converted to HRV signal. In the results, emotional stress of subjects produced an increase in sympathetic activity. Sympathetic activation was responsible for the significant increase in the LF/HF ratio. Subjects were divided into two groups with task ability. Subjects who have higher mental stress have lack of task ability.

• Science of Emotion and Sensibility
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• v.12 no.3
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• pp.289-298
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• 2009

An analysis was carried out for this study to figure out if there exists any differences in the model consumers accept for commercialized MP3-playing clothing and bio-signal sensing clothing. To analyze the differences of the structural variables of the products types, t-test was conducted with SPSS 15.0 package and multi-group analysis with AMOS 5.0 to find out the differences of each path goes with product types in structural equation model. In analytical results of effective sample of 557 copies of questionnaire, consumers' were highly aware of MP3-playing clothing in perceived ease of use, while they were aware relatively high of bio-signal sensing clothing in perceived usefulness, attitudes, consumer acceptance. The perceived value which was input to find out consumers awareness about sale price of commercialized products, was proven to do very important moderating role in forming consumers' attitudes and acceptance intention. Besides, consumers showed a difference in path in accepting model goes with product types. In bio-signal sensing clothing case, 'the perceived usefulness$\rightarrow$attitudes' path which was backed up in MP3-playing clothing was rejected, and 'perceived value$\rightarrow$attitudes' path appeared relatively high with moderating role of perceived value higher than MP3-playing clothing. Considering the results above, as the smart clothing is in the initiative commercialization stage while consumers were in the inquiry stage into awareness or information necessary in the course of purchase decision-making, and so an effective commercialization strategy seems to be necessary.