• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.696-698
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• 2009

현대인들의 감정 불안정 상태로 인한 질병과 사회문제, 범죄 등이 많은 이슈로 나타나고 있다. 본 논문은 사람의 감정에 따른 생리적인 변화를 생체신호를 측정하여 분석하고 이를 이용하여 사람의 감정을 조절할 수 있는 음악을 선택하여 조절한다. 사람의 심박수를 측정하고 그 데이터를 HRV(심장박동 변화율)로 변환하면 그 사람의 현재 기분을 추정해 낼 수 있다는 연구 결과를 적용하여 기분에 따라 알맞은 음악을 자동으로 선택 하여 들려줄 수 있는 시스템을 설계하고 구현한다.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.103-112
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• 2018

In this paper, we propose a method of recognizing emotions using PPG sensor which measures blood flow according to emotion. From the existing PPG signal, we use a method of determining positive emotions and negative emotions in the frequency domain through PSD (Power Spectrum Density). Based on James R. Russell's two-dimensional prototype model, we classify emotions as joy, sadness, irritability, and calmness and examine their association with the magnitude of energy in the frequency domain. It is significant that this study used the same PPG sensor used in wearable devices to measure the top four kinds of emotions in the frequency domain through image experiments. Through the questionnaire, the accuracy, the immersion level according to the individual, the emotional change, and the biofeedback for the image were collected. The proposed method is expected to be various development such as commercial application service using PPG and mobile application prediction service by merging with context information of existing smart phone.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.29-38
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• 2024

After Transcatheter Aortic Valve Replacement (TAVR), comprehensive management of complications, including the need for Permanent Pacemaker Implantation (PPI), is crucial, increasing the demand for accurate prediction models. Departing from traditional image-based methods, this study developed an optimal PPI prediction model based on ECG data using the XGBoost algorithm. Focusing on ECG signals like DeltaPR and DeltaQRS as key indicators, the model effectively identifies the correlation between conduction disorders and PPI needs, achieving superior performance with an AUC of 0.91. Validated using data from two hospitals, it demonstrated a high similarity rate of 95.28% in predicting PPI from ECG characteristics. This confirms the model's effective applicability across diverse hospital data, establishing a significant advancement in the development of reliable and practical PPI prediction models with reduced dependence on human intervention and costly medical imaging.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.269-278
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• 1998

This paper describes a development of efficient stress ECG signal analysis algorithm. The algorithm consists of wavelet adaptive filter(WAF), QRS detector and ST segment detector. The WAF consists of a wavelet transform and an adaptive filter. The wavelet transform decomposed the ECG signal into seven levels using wavelet function for each high frequency bank and low frequency bank. The adaptive filter used the signal of the seventh lowest frequency band among the wavelet transformed signals as primary input. For detection of QRS complex, we made summed signals that are composed of high frequency bands including frequency component of QRS complex and applied the adaptive threshold method changing the amplitude of threshold according to RR interval. For evaluation of the performance of the WAF, we used two baseline wandering elimination filters including a standard filter and a general adaptive filter. WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of results of QRS complex detection, we compared our algorithm with existing algorithms using MIT/BIH database. Our algorithm using summed signals showed the accuracy of 99.67% and the higher performance of QRS detection than existing algorithms. Also, we used European ST-T database and patient data to evaluate measurement of the ST segment and could measure the ST segment adaptively according to change of heart rate.

• Review of KIISC
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• v.25 no.4
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• pp.43-50
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• 2015

바이오인식기술은 사람의 지문 얼굴 홍채 정맥 등 신체적 특징(Physiological characteristics) 또는 음성 서명 자판 걸음걸이 등 행동적 특징(Behavioral characteristics)을 자동화된 IT 기술로 추출 저장하여 다양한 IT 기기로 개인의 신원을 확인하는 사용자 인증기술이다. 2001년 미국의 911 테러사건으로 인하여 전 세계 국제공항 항만 국경에서 지문 얼굴 홍채 등 바이오정보를 이용한 출입국심사가 보편화됨과 동시에 ISO/IEC JTC1 SC37(바이오인식) 국제표준화기구를 중심으로 표준화가 급속도로 진행되어 왔다. 최근 들어 스마트폰 테블릿 PC 등 모바일기기에 지문 얼굴 등 바이오정보를 탑재하여 다양한 모바일 응용서비스를 가능하게 해주는 모바일 바이오인식 응용기술이 전 세계적으로 개발 보급되고, 삼성전자 페이팔 중심으로 바이오인식기술을 이용한 모바일 지급결제솔루션에 대한 사실표준화협의체인 FIDO, ITU-T SG17 Q9(텔레바이오인식) 국제표준화기구를 중심으로 표준화가 진행되고 있다. 특히 이러한 모바일 바이오인식기술은 스마트폰을 통한 비대면 인증기술 수단으로서 핀테크의 중요한 요소기술로 작용될 전망이다. 한편, 위조지문 등 전통적인 바이오인식 기술의 위변조 위협으로 인한 우려도 증폭됨에 따라 스마트워치 등 웨어러블 디바이스에서 살아있는 사람의 심박수(심전도), 뇌파 등의 생체신호를 측정하여 스마트폰을 통하여 개인을 식별하는 차세대 바이오인식기술로 진화중에 있다. 본고에서는 바이오인식기술의 변천사와 함께 국내외 모바일 바이오인식기술 동향과 표준화 추진현황을 살펴보고, 지난 2015년 5월 29일 발족한 KISA "모바일 생체신호 인증기술 표준연구회"를 통하여 뇌파 심전도 등생체신호를 이용한 차세대 바이오인식 기술 및 표준화 계획을 수립하여 향후 바이오인식기반의 비대면 인증기술에 대한 추진전략을 모색하고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.11-18
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• 2015

Stress is one of the major causes threatening the mental and physical health of human today. In this paper, we analyzed the heart rate variability(HRV), galvanic skin response(GSR), and skin temperature data measured from the university subjects before and after the class to examine the influence on bio-signal in stress environment. Thirty subjects from university students (aged between 21 and 27 years; mean=22.31, STD=1.45) took part in this study. From the experiment results, RMSSD(p=0.033), LF peak(p=0.003), VLF(p=0.045) were statistically significant from those of the control group(p<0.05) of HRV both in time and frequency domain. We observed that mean skin conductivity after the class(mean=5.993(uS), SD=3.406) is higher than that before the class(mean=3.039(uS), SD=2.628) by 97.2% on average and the skin temperature after the class($34.835{\pm}0.305$) is slightly higher than that before the class($34.471{\pm}0.281$) by 1.055% on average. The results in this research could be used to examine the autonomic response in clinical stress related research.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.171-178
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• 2017

'Smart Sensibility Mat (SSM)' was developed and manufactured for positive sensibility of newborn with fiber, digital, and sensibility technology to reflect features and advantages of kangaroo care. For tactile stimuli, the tube of the silicon material to provide a constant temperature of $32^{\circ}C$ was inserted into the mat and connected to the water-thermostat. To provide a uniform temperature throughout the mat, the fabric by the inserting conductive yarn was attached to the mat surface. After wrapping the mat with cotton pad, the polyurethane foam used as medicine in order to similar to the human skin was bonded to the surface of the mat. To provide the auditory stimuli of a level of 30dB with mother's heartbeat sounds and voice recorded in advance, the Bluetooth speaker was inserted into the mat. To investigate the effects of SSM, 10 newborns who born within two weeks were involved in this experiment. While the baby was lying on each of the general mat (GM) and SSM, the baby's physiological signals-heart rate, breathing rate, temperature- were measured and then, those were conducted t-test to examine the difference between the signals of SSM and GM. The results were as follows: heart rate (t=8.131, p<.001) and respiratory rate (t=7.227, p<.001) among the physiological signals of SSM decreased significantly than GM within the normal range. Temperature (t=1.062, p=0.292) at SSM showed a tendency to decrease than GM within the normal range. This means the tactile stimuli and the auditory stimuli providing from SSM give stable physiological responses. Thus, SSM leads to have psychological comfort and stability of newborns.