• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.660-662
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• 2016

최근 생체 인식이 도입된 스마트폰, 태블릿, 웨어러블 디바이스들의 수요가 증가함에 따라 생체를 이용한 인식 등에 대한 연구가 활발히 진행되고 있다. 이에 본 논문에서는 사물인터넷과 PPG(광용적맥파)를 사용한 사용자의 인증 시스템을 제안하였다. 제안 시스템은 아두이노에 연결된 PPG센서를 통하여 PPG신호를 측정하여 측정한 신호를 서버로 보내게 된다. 서버에서는 이 신호로부터 특징점을 추출하여 기존에 학습된 정보를 바탕으로 K-NN 판별 알고리즘을 통하여 인증 절차를 거치게 된다. 실험결과, 제안 시스템은 평균 약 71.42%의 인식률을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.648-649
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• 2024

웨어러블 디바이스 착용자의 PPG 신호 데이터로 위협 상황을 감지하는 알고리즘을 개발한다. 본 논문에서는 외부 환경에 예민한 PPG 센서에 최적화된 전처리 알고리즘을 제안하고 긍정 및 부정 영상 시청 실험을 통해 얻은 PPG 신호 데이터를 이용하여 위험 상황과 안전한 상황을 구분하는 정확도 96.87%의 1D-CNN 모델을 개발한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.161-167
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• 2017

This paper presents an effective PPG measurement device in a wrist watch for smart healthcare. PPG can be acquired by measuring absorption of light and PPG measurement device is classified as transmission and reflection type. Reflection type is easy to implement but vulnerable to motion artifact. Moreover, reflection type must use 2 hands at once if the device is implemeted in a wrist watch. This paper describes the implementation of robust transmission type PPG measurement device by using previous developed reflection type device. PPG sensor is clip type and can be separated from the device so that the influence of motion artifact can be reduced. The proposed device can transmit the measured signal in real-time and is useful for smart healthcare. For evaluation of the proposed device, PPG signals are compared with reflection type in the same condition. The results show that the proposed device has 20% improvement in performance.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.17-24
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• 2010

This paper describes a real time reliable monitoring method and analysis system using wrist type oximeter for ubiquitous healthcare service based on IEEE 802.15.4 standard. Photoplethysmograph(PPG) is simple and cost effective technique to measure blood volume change. In order to obtain and monitor physiological body signals continuously, a small size and low power consumption wrist type oximeter is designed for the measurement of oxygen saturation of a patient unobtrusively. The measured data is transferred to a central PC or server computer by using wireless sensor nodes in wireless sensor network for storage and analysis purposes. LabVIEW server program is designed to monitor stress indicator from heart rate variability(HRV) and process the measured PPG to accelerated plethysmograph(APG) by appling second order derivatives in server PC. These experimental results demonstrate that APG can precisely describe the features of an individual's PPG and be used as estimation of vascular elasticity for blood circulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.992-999
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• 2009

The pulse transit time(PTT), which is determined by measuring the electrocardiogram(ECG) and pulse wave, gives comprehensive information about the cardiovascular system. However, a little movement of body and/or inaccurate pressure applied to skin during the measurement of pulse wave leads to acquire incorrect results. To overcome such problem, we developed an integrated sensor system which makes it possible to measure ECG, pressure pulse wave(PPW) and photoplethysmograph(PPG) at the same time. Futhermore, we implemented a new metal electrode which enables to continuously measure ECG. We verified that both integrated sensor system and new electrode provide useful effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.627-629
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• 2023

생체 신호를 분석하여 사용자의 건강과 정신 상태를 예측하고, 관련 질병에 관해 예방하는 연구가 늘어나고 있다. 생체 신호 중 심박은 사람의 육체, 정신적인 상태를 반영하는 대표적인 신호이지만 기존의 접촉 패드를 통한 ECG나 광학 센서를 통한 PPG로 심박을 예측할 때는 구속적인 환경이 필요하여 일상적인 상황 속에 적용하기 어려웠다. 이러한 단점을 해결하고자 본 논문은 UBFC-RPPG 데이터셋의 동영상 프레임을 RGB 채널마다 다른 가중치를 적용하는 전처리를 하여 학습 데이터의 크기를 줄이면서 정확도를 높이고, 3D-CNN을 활용한 딥러닝으로 순간적인 영상에서도 PPG 신호를 예측할 수 있도록 1초 전처리 영상을 학습한 후, 신호를 예측하는 것을 목표로 한다. 이렇게 비접촉식으로 취득된 신호는 더 다양한 환경에서의 감정분류, 우울증 진단, 질병 감지 등 다양한 분야에 활용될 수 있다.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.322-328
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• 2011

There are many ways to detect the heart rate non-invasively such as ECG, PPG, strain gauge, and pressure sensor. In this paper, the pulse wave measurement system using bipolar biased head on mode of the Hall sensor is proposed for measuring the radial artery pulse. TMS320F2812 was used to implement the proposed system and a portable wireless network(zig-bee) was used to show the experimental result. It was confirmed from experiment that the performance of the implemented system was more stable and faster than PPG sensor or piezoelectric film pressure sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.813-820
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• 2009

This paper describes the realization of ubiquitous healthcare monitoring system using wearable pulse oximeter based on a wireless sensor network. In order to obtain information of oxygen saturation from a patient, a small size and low power consumption wearable pulse oximeter was designed. Information of oxygen saturation collected by wireless sensor node was transmitted wirelessly to a base-station for storage and display purposes via wireless sensor network. Wireless sensor nodes were programmed by TinyOS application to perform data acquisition and transmission. Lab VIEW server program was designed to monitor information of oxygen saturation and process the measured PPG (photo plethysmogram) signals to APG(Accelerated plethysmogram) signals by appling second order derivatives. PPG signals are simple and cost effective technique to measure blood volume change.