• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.603-606
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• 2008

본 연구에서는 가정 또는 사무실에서 무구속(non-restrained)적인 방법으로 심장의 활동상태를 모니터링하기 위하여 심탄도(ballistocardiogram, BCG)를 계측하고자 하였다. 심탄도는 심전도의 측정과는 다르게 신체에 전극을 부착할 필요가 없고, 무구속 상태에서 신호계측이 가능하므로 장시간 동안 심장상태의 모니터 링에 유용하게 활용할 수 있는 장점이 있다. 따라서 본 연구에서는 무구속 심탄도 계측을 위하여 의자형 심탄도 측정시스템을 구현하였다. 먼저 로드셀을 의자의 상판과 하판사이에 설치하여 피검자의 체중을 측정할 수 있는 센서부를 구성하였으며, 센서로부터 출력되는 신호를 증폭 및 필터링하기 위한 계측부를 구현하였다. 구현된 심란도 계측시스템의 성능평가를 위하여 피검자 10명을 대상으로 심란도 계측평가를 수행하였으며, 이때 심전도 신호와 동시계측을 수행하였다. 평가결과 본 연구에 의해 구현된 심탄도 계측시스템의 우수한 계측성능을 확인할 수 있었다.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.8-16
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• 2010

This research proposes measuring of BCG(ballistocardiogram) to monitor heart activities in a non-constrained environment, at home or work. Unlike with ECG, measuring BCG does not require the attachment of leads on the subject's body and allows signal measuring in a non-constrained state. It enables effective long-term monitoring of cardiac conditions. In this study a chair type BCG measurement system to continuous monitor the activity of the heart is implemented. The instrument consists of upper petal and ready for press of chair load cell sensor is attached to measure the change of the object's weight. In order to extract the output ballistic signal from the weight and force sensor signals. Beside the signal processing circuit for the digital conversion, the ballistic signal is detected using DAQ equipment. Signal processing algorithm including wavelet transforms for noise cancellation, template matching for normalization and peak detection in BCG is developed. ECG and BCG were concurrently measured to evaluate the performance of the system, and comparing the characteristics of the two signals verified the possibility of the system in non-constrained and nonconscious health monitoring.

• Journal of Biomedical Engineering Research
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• v.26 no.2
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• pp.95-99
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• 2005

We have performed EEG current source imaging on the cortical surface using visual evoked potentials (VEPs) recorded inside a 3.0 T MRI magnet. In order to remove ballistocardiogram (BCG) artifacts in the VEPs, an improved BCG template subtraction technique is devised. Using the cortically constrained current source imaging technique and pattern-reversal visual stimulations, we have obtained current source maps from 10 subjects. To validate the EEG current source imaging inside the magnet, we have compared the current source maps to the ones obtained outside the magnet. The experimental results demonstrate that there is a strong correspondence between the current source maps, proving that current source imaging is feasible with the evoked potentials recorded inside a 3.0 T MRI magnet.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.3
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• pp.104-109
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• 2018

A non-intrusive ECG and BCG measurement system is introduced. The system is built on a auxiliary backrest of a chair. The developed system is aimed to non-intrusive assessment of cardiovascular dynamic indices such as pulse arrival time(PAT) and pre-ejection period (PEP). In the system, capacitive active electrodes and capacitive grounding were used for the non-intrusive indirect-contact ECG measurement, and EMFi pressure sensor was used for the non-intrusive BCG measurement. The capacitive active electrodes and the EMFi sensor were attached on the backrest. Using the system, ECG and BCG were successfully acquired. The measured BCG showed peaks that following ECG R peaks. It was shown that the time interval between Q wave in ECG and first peak in BCG correlates Pre-ejection period measured by impedance-cardiogram. The results showed that the introduced system can be used for the non-intrusive various cardiovascular information including ECG, PAT, PEP.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.169-176
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• 2014

As body postures on bed affects various sleep related diseases, it is considered as important information when monitoring sleeping in daily life. Though there have already been a few approaches to monitor body postures on bed conventionally, the development for simple and unconstrained methods is still needed to realize the long-term daily monitoring. Focusing on the fact that ballistocardiogram changes depending on the body postures on bed, we developed a novel method to estimate body posturesusing extremely simple, film-type ballistocardiogram sensor which is based on polyvinylidene fluoride(PVDF) film. With 10 subjects, we performed two experiments. One was for an estimation test to show that body postures on bed can be estimated by ballistocardiogram, and the other was for a reproducibility test to present the feasibility of ballistocardiogram based body postures monitoring. To estimate body postures on bed, we made an individual template set of body postures by designating one ballistocardiogram (BCG) sample as a template in each postures. Then, we calculated Pearson's correlation coefficients between a sample and each templates and estimated the body posture of the sample by choosing a posture which corresponds to the most significant correlation coefficients. As a result, we estimated body postures on bed with 99.2% accuracy in average and found that the estimation using ballistocardiogram is reproducible.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.118-123
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• 2011

Heart related diseases mainly caused by heavy work load and increasing stress in human daily life. Therefore, researches on mobile healthcare monitoring for daily life has been carried out. Notably, wearable healthcare monitoring system which has least restriction has been tried to provide an emergency alert of abnormal heart rate. In this study, we developed chair type unconstrained BCG measurement system which able to perform continuous heart status monitoring at the office and daily life in the unconstrained way. Furthermore, adaptive threshold is used to detect the heart rate from BCG signals. The HRV(heart rate variability) is calculated from heart rate interval. ECG signal measured using conventional method and BCG signal measured using unconstraint system are carried out simultaneously for the purpose of performance evaluation. From the comparison result, BCG signal shows a similar heart beat characteristic as ECG signal. This proves the possibility of practical implementation of unconstraint healthcare monitoring system. In addition, medical examination like valsalva maneuver is performed to observe the changes in HRV due to stress. By performing valsalva maneuver, heart is said to be placed under an artificial physical stress condition. Under this artificial physical stress condition, the time and frequency domain of HRV parameters are evaluated.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.235-239
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• 2008

일상생활 중 지속적인 심장의 활동상태 모니터링을 통해 건강관리 및 응급상황에 대처하기 위한 많은 관련 연구들이 수행되고 있다. 심전도신호를 모니터링하기 위해서는 필수적으로 전극을 신체에 부착해야만 하며 피검자로 하여금 불편함을 초래한다. 본 연구에서는 가정 또는 사무실에서 무구속(non- restrained)적인 방법으로 심장의 활동상태를 모니터링하기 위하여 심탄도(ballistocardiogram, BCG)를 계측하고자 하였다. 심탄도는 심장의 수축과 이완에 따라 심장과 혈관에서의 혈류변화에 따른 탄도를 계측한 신호를 의미하며, 심탄도를 계측하기 위하여 로드셀을 이용한 의자형 무구속 심탄도 측정시스템을 구현하였다. 그리고 심탄도 신호로부터 건강정보의 추출을 위해 웨이브렛 변환과 템플릿 매칭(template maching)기법을 이용한 신호처리기법을 제안하였다. 구현된 시스템 및 신호처리기법의 타당성을 검토하기 위해 심전도와 동시에 심탄도를 측정하였으며, 상호 비교를 통해 심탄도 계측시스템의 유용성을 평가하였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.163-170
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• 2022

Currently, as the number of families living with companion animals increases, the demand for information about the health status of companion animals has increased. As the demand for this increases, there is a need for a method to measure respiration and pulse in companion animals. Considering the characteristics of hairy companion animals, we want to measure respiration and pulse signals using BCG, which is different from adsorption ECG. Since this BCG method is made by mixing respiration and pulse signals into one signal, it is necessary to separate the respiration signal waveform and the pulse signal waveform from one signal waveform. In this paper, a wearable device for BCG measurement was implemented to measure the signal, and a method of separating the signal input from the BCG wearable device into a respiration signal and a pulse signal was proposed.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.