• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.591-598
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• 2016

For the mobile healthcare environment, it is important to measure the exact biomedical signals in real time, and another key point is to design mobile healthcare devices with low power consumption. In this paper, we propose a method in which the piezo film sensor(PFS), having a low power characteristic, is used to measure the pulse signal synchronized with the heart rate from the radial artery. The critical issue in the bio-signal processing is the existence of the motion artifacts. To dissolve this problem, we have applied the periodic moving average filter using the quasi-periodicity of the pulse signal in addition to the conventional method of the adaptive filtering using the reference signal. Results of simulation and experiments are presented to confirm that the quasi-periodicity of the PFS signal can be used to eliminate completely the motion artifacts which still appears after the adaptive filtering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5847-5855
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• 2013

The purpose of this research is to develop Dementia-diagnosis application program through inter-working between biometric sensors and smart phone with diagnostic programs. Receiving data from 2 biometric sensors, SpO2 and HRV(heart rate variability) with Bluetooth and adding scores from questionnaires and memory test game, smart phone can draw a final Dementia Index(DI). Simple clinical tests provided threshold for Dementia, and diagnosis was made by comparing the DI with the threshold. We performed testing on persons aged over 60 and found out the proposed application can be used to diagnose Dementia easily and quickly. Additionally, we have shown the potential of oxygen chamber combined with Dementia-diagnosis application as a medical equipment to ease or cure Dementia in its early stages.

• Journal of Sensor Science and Technology
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• v.9 no.1
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• pp.51-60
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• 2000

This paper outlines the development of a non-pulsatile axial flow type blood pump control system. By utilizing blood pressure and heart rate, this system can assist the left ventricle in controlling blood pressure and blood volume. The system is comprised of a blood pump, signal sensor, signal interface, and signal-processing component. A control algorithm is also proposed which can control non-pulsatile, continuous blood flow in the human circulatory system. To facilitate the control required for non-pulsatile blood pump in a physiological system, an experimental control rule was developed utilizing ECG and blood pressure data, both of which are easily detectable variables in the body. The system was then tested using a mock-up circulation system and we found that it is possible that this systems could be temporarily used in clinic.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.45-50
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• 2019

When PPG signal is used in mobile healthcare devices, the accuracy of the measured heartbeat decreases from the influence by the movement of the user. The reason is that the frequency band of the noise overlaps the frequency band of the PPG signal. In order to remove these same noises, the methods using frequency analysis method or application of acceleration sensor have been investigated and showed excellent performance. However, in applying these methods to low-cost healthcare devices, it is difficult to apply these methods because of much processing time and sensor's cost. In order to solve these problems, this study proposed the filter design method using an adaptive notch filter and the fuzzy inference system to extract more accurate heart rate in real time and evaluated its performance. As results, it showed better results than the other methods. Based on the results, when applying the proposed method to design the mobile healthcare device, it is possible to measure the heartbeat more accurately in real time.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.78-82
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• 2006

The ventricular assist device(VAD) helps to reduce the overload against the patient's native heart(NH). The pulsatile VAD pumps out the ventricular blood to the aorta with pulsatile flow. If the VAD pulsates simultaneously with the NH, the ventricle of the NH could confronts abnormally elevated aortic pressure, and this could deteriorate the ventricle rather than assist to recover it. Thus counterpulsation algorithms to avoid copulsation have been adopted by many VADs, but these methods utilize electrocardiography or arterial pressure signals, which may have difficulties to acquire consistently for a long period. In this study, the copulsation estimation algorithm for the counterpulsation is developed using the VAD outlet pressure signal. The VAD outlet pressure signal is good to maintain for a long time and the sensor part could be integrated to the VAD as a built-in module. From the VAD outlet pressure signal and its pump rate information calculated with Fast Fourier Transform, pulse peaks by the VAD and the NH were extracted and the next copulsation time at which the VAD and the NH would pulsate simultaneously was estimated. This estimation algorithm was implemented by using PC MATLAB software and tested for various pump rate conditions with mock circulation system. For each condition, the copulsation time was estimated successfully. Consequently, the results showed the possibility to use the outlet cannula pressure signal in the copulsation estimation.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

• 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 the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.226-232
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• 2016

The research for the improvement in math and science scores is active by the brain exercises, stress reliefs, and emotion sensitized illuminations. This principle is based on the following facts that the most effective brain turns are supported with the circumstances not only when the brain wave should keep stability and comfort in science criticism, but also when minimized stress and comfortable illumination should be adjusted in solving math problem. In this paper, in order to effectively learn mathematics and science, the most optimized simulating tests in learning conditions are conducted by using a stress relief. However, depending on the users' tastes, the effectiveness on favorite music or colors therapy have no convergency but many differentiations. Therefore, in this paper, in order to solve this problem, the proposed optimal illumination and music therapy treatment using fuzzy inference method.

• 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.