• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1427-1428
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• 2015

MEMS(Micro Electro-mechanical System) are getting attention as promising industry in the 21st century. Car air bags, acceleration sensors, and medical, information appliances are being actively applied in MEMS. This paper suggest the electrical electrodes of brain signal applied MEMS model and the prototype design for EEG signal amplification circuit. Also, we suggest an independent BCI(Brain Computer Interface) system with brain electrical signal of electrode models and wireless communication platform.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.117-125
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• 2015

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.382-387
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• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.571-577
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• 2017

Through the 2015 Revised Curriculum, programming education is introduced into the elementary school regular curriculum as part of the software education. Effective teaching & learning methods can be presented through an analysis of the effects of programming education on the problem-solving abilities. In this paper, students were divided into two groups according to their academic achievement, a learning program was developed for five times of implementation using the entry and the sensor board for the entry, and classes to which it was applied were conducted. Before and after the classes, a problem-solving test tool was used to measure and analyze the changes in Gamma waves and EEG concentration indicators. As a result, the gamma waves and the concentration indices of the students in the group with high academic achievement showed a tendency to be improved through the programming lessons, and those of the students in the group with poor academic achievement showed no such tendency. Through this, the necessity of the level-specific programming education in consideration of students' academic abilities was suggested.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.2
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• pp.10-20
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• 2002

Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.1009-1011
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• 2013

최근 U-헬스케어 서비스가 증가함에 따라 예방과 건강증진 등에 관심과 연구가 활발히 이루어지고 있다. 이러한 U-헬스케어에 사용되는 ECG, EMG, PPG EEG등이 스마트폰 기반의 휴대용 장비와 연동된 센서들의 연구 역시 활발히 진행 중이다. 일반적으로 PPG신호는 적외선센서를 이용하여 모세혈관의 혈중 헤모글로빈 농도 변화에 의한 맥파를 측정한다. PPG신호의 잡음을 제거하기 위해 필터를 사용하였다. 필터된 정보를 ADC하여 스마트폰으로 BlueTooth통신을 이용하여 전송한다. 최종적으로 스마트 디바이스에 PPG신호를 그린다. 제작결과 PPG아날로그 신호를 보는 것과 동일하게 스마트 디바이스에 그릴 수 있음을 보였다. 이후 PPG신호를 이용하여 혈관건강도 및 건강예방의 측정의 연구를 계속해서 한다.

• Journal of Sensor Science and Technology
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• v.4 no.3
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• pp.30-36
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• 1995

One of principal causes of car accidents is low arousal level of driver. Drivers arrive their destination under an appropriate arousal level. Basic research was done to develop a portable arousal monitor and to evaluate the arousal level of drivers. An arousal monitor which can simulantaneously measure the skin impedance change and EEG was designed and tested. The relationship among three parameters was studied and was used to determine the index of skin impedance level depending on arousal level.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1075-1077
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• 2015

최근 마이크로 컨트롤러를 활용한 사물인터넷의 분야가 급격하게 증가하면서, 이를 활용한 헬스케어 분야에 대한 관련 연구 역시 활발하게 진행되고 있다. 마이크로 컨트롤러를 활용한 헬스케어 시스템 장치의 장점 중 하나는, 사용자가 스스로의 건강관리를 위해 일상생활에서의 습관이나 행동 양식을 손쉽게 확인할 수 있다는 점이다. 제안한 시스템은 가벼운 수면장애를 갖고 있는 사용자를 대상으로 실제 수면 환경에서의 뇌파를 측정 및 분석하여 수면 환경 내의 빛 환경을 자동으로 제어할 수 있도록 하였다.