• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.141-150
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• 2006

Since the late 1990s, 'smart clothing' has been developed in a various way to meet the need of users and to help people more friendly interact with computers through its various designs. Recently, various applications of smart clothing concept have been presented by researchers. Among the various applications, smart clothing with a health care system is most likely to gain the highest demand rate in the market. Among them, smart clothing for check-up of health status with its sensors is expected to sell better than other types of smart clothing on the market. Under this circumstance, research and development for this field have been accelerated furthermore. This research institution has invented biometric sensors suitable for the smart clothing, and has developed a design to diagnose various diseases such as cardiac disorder and respiratory diseases. The newly developed smart clothing in this study looks similar to the previous inventions, but people can feel more comfortable in it with its fabric interaction built in it. When people wear it, the health status of the wearers is diagnosed and its signals are transmitted to the connected computer so the result can be easily monitored in real time. This smart clothing is a new kind of clothing as a supporting system for preventing various cardiac disorder and respiratory diseases using its biometric sensor built-in, and is also an archetype to show how smart clothing can work on the market.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1141-1148
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• 2016

This paper proposed a heart attack predictive monitoring system using QRS pattern of ECG for wearable healthcare. It detects abnormal heart pattern with a ECG (X, Y) coordinate pattern DB on wearable monitoring smart watch. We showed the acute heart failure prevention system and method with a proposed scheme. Especially, It proved the method which can do first aid in gold time through abnormal heart analysis with a digital ECG(X, Y) pattern information when acute heart failure occurs.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2956-2958
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• 2005

현대 사회는 의료 기술의 발달로 인한 인간 수명의 연장과 핵가족화로 인하여 혼자 지내는 노인이 많아지면서 응급상황 발생시 신속한 의료서비스를 받지 못하는 경우가 많아지고 노령화로 인한 심장관련 질환이 급격하게 증가하고 있다. 본 논문에서는 심전도, 체온, 움직임 등의 생체신호를 획득하고 획득된 생체신호를 분석하여 응급상황 발생시 응급의료센터와 보호자에게 구조 메시지를 보낼 수 있는 시스템을 제안하였다. 시스템은 크게 생체신호 획득을 위한 생체신호 획득부와 생체신호 처리 및 전송, 디스플레이를 위한 모바일 부로 나눌 수 있으며 블루투스를 통하여 서로 통신한다. 또한 모바일 부에서 처리된 신호는 802.11b WLAL을 통하여 PC의 데이터베이스에 저장된다.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.381-391
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• 2017

Biometric technology is a technology for authenticating a user using the physical or behavioral features of the inherent characteristics of the individual. With the necessity and efficiency of the technology in the fields of finance, security, access control, medical welfare, inspection, and entertainment, the service range has been expanding. Biometrics using biometric information such as fingerprints and faces have been exposed to counterfeit and disguised threats and become a social problem. Recent studies using a bio-signal from the inside of the body other than the bio-information of the external body are being developed. This paper analyzes the recent research and technology of biometric systems using bio-signals, ECG, heart sounds, EEG, and EMG to present the skills needed for the development direction. In the future, utilizing the deep learning to build and analyze database to manage bio-signal based big data for the complex condition of individuals, biometrics technologies suitable for real time environment are expected to be researched.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.813-821
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• 2013

In this paper, a vital signal sensor based on impedance variation of resonator is presented. Proposed vital signal sensor can detect the vital signal, such as respiration and heart-beat signal. System is composed of resonator, oscillator, surface acoustic wave (SAW) filter, and power detector. The cyclical movement of a dielectric such as a human body, causes the impedance variation of resonator within the near-field range. So oscillator's oscillation frequency variation is effected on resonator's resonant frequency. SAW filter's skirt characteristic of frequency response can be transformed a small amount of frequency deviation to a large variation. Aim to enhance the existing sensor detection range, proposed sensor operates in 870 MHz ISM band, and detect respiration and heart-beat signal at distance of 120 mm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.611-616
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• 2023

ECG(electrocardiogram) is a test used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, the noise of the ECG signal was removed using the digital IIR Butterworth low-pass filter. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, was compared using the deep learning model of LSTM, it was confirmed that the activation function with the smallest error was the tanh() function. Also, When the performance evaluation and elapsed time were compared for LSTM and GRU models, it was confirmed that the GRU model was superior to the LSTM model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.563-568
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• 2023

ECG(electrocardiogram) is used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, Noise included in the ECG signal was removed by using a lowpass filter of the Digital FIR Hamming window function. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, which was confirmed that the activation function with the smallest error was the tanh() function, the elapsed time was longer when the batch size was small than large. Also, it was confirmed that result of the performance evaluation for the GRU model was superior to that of the LSTM model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.626-629
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• 2001

인간의 뇌에는 천 억개 이상의 신경세포들이 있다. 이들은 신경작용의 매우 복잡한 네트워크를 통해 서로서로 연결되어져 있다. 하나의 신경세포로부터 다른 신경세포로 신호가 전달되는 과정은 다른 화학 전달물질들에 의해 이루어지며 신호 전이는 시냅스라고 불리는 신경세포간의 특정 접촉부 위에서 일어난다. 뉴런의 신호전달 체계에 대한 연구는 20세기 초반에 본격적으로 이루어져 왔으며, 현재는 각 뉴런에 대한 정확한 신호전달 원리를 밝히는데 많은 연구가 이루어지고 있다. 최근에 연구가 활발하게 이루어지고 있는 신경전달 물질중 하나인 일산화 질소는 인간의 세포에 노출되었을 경우 세포막을 기준으로 농도 차가 발생하여 근육이 이완되는 현상을 유발한다. 이런 세포막을 기준으로 한 운동신경 변화, 심장박동의 변화, 근육의 이완철상 및 치명적인 이상을 초래하는 현상을 GENESIS를 이용하여 시뮬레이션 해 보았다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.893-895
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• 2016

세계적으로 헬스케어 산업의 발전 가능성은 눈에 띄게 증가하고 있다. 그 중에서도 환자 혹은 각종 디바이스 사용자의 생체신호를 다루는 기술은 다양한 중요정보를 얻을 수 있다. 본 논문에서는 심전도의 미세한 생체 전위를 측정하기 위해 각종 필터와 증폭기를 이용하여 회로를 설계하고 이를 Cortex-M3 Microprocessor와 MATLAB 프로그램을 이용하여 필터링과 데이터통신을 통해 최종적으로 실시간으로 모니터링 하였다. 일반적으로 임상이나 진단에 이용되는 ECG 신호는 각종 심장질환의 지표로 사용되지만 전문적인 지식을 갖추지 않은 일반 사용자가 사용하기에는 어려운 점이 없지 않아 있다. 따라서 이 연구는 아날로그 신호를 디지털 신호로 변환하여 생체신호를 다루는 다양한 분야에서 용이할 수 있다.