• Korean Journal of Artificial Intelligence
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• v.8 no.1
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• pp.1-6
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• 2020

The Recently there has been a growing interest in health care due to the COVID-19 situation. In this paper, we intend to develop a healthcare monitoring system to provide users with smart healthcare systems in line with the healthcare 3.0 era. The system consists of a wireless network between various sensors, Android smartphones, and OLEDs using Bluetooth, and through this, a health care monitoring system capable of collecting user's biometric information and managing health by receiving data values of sensors connected to Arduino. In conclusion, the user's BPM value was calculated using the heart rate sensor, and the exercise intensity can be adjusted through this. In addition, a step derivation algorithm is implemented using an acceleration sensor, and calorie consumption can be measured using the step and weight values. As such, the heart rate, step count, calorie consumption data can be transmitted to a smartphone application through a Bluetooth module and output, and can be output to an OLED for users who are not easy to access the smartphone. This healthcare monitoring system can be applied to various groups and technologies.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.373-381
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• 2005

The respiratory and heart beat signals are the fundamental physiological signals for sleep monitoring in the home. Using the air mattress sensor system, the respiration and heart beat movements can be measured without any harness or sensor on the subject's body which makes long term measurement difficult and troublesome. The differential measurement technique between two air cells is adopted to enhance the sensitivity. The concept of the balancing tube between two air cells is suggested to increase the robustness against postural changes during the measurement period. With this balancing tube, the meaningful frequency range could be selected by the pneumatic filter method. The mathematical model for the air mattress and balancing tube was suggested and the validation experiments were performed for step and sinusoidal input. The results show that the balancing tube can eliminate the low frequency component between two cells effectively. This technique was applied to measure the respiration and heart beat on the bed, which shows the potential applications for sleep monitoring device in home. With the analysis of the waveform, respiration intervals and heart beat intervals were calculated and compared with the signal from conventional methods. The results show that the measurement from air mattress with balancing tube can be used for monitoring respiration and heart beat in various situations.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.18-26
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• 2009

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.193-198
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• 2017

Recently, as interest in health increases, various wearable devices such as smart watch and smart band which can measure user's biometric information are being studied. Conventional wearable devices service the measured biometric information in a form that provides simple monitoring, disease prevention, and exercise amount. However, the user is Lack to deal with the dangerous situation. In this paper, we propose a hazard notification system to address these problems. The biometric information measured by the acceleration sensor and the heart rate sensor is transmitted to the application through the Arduino in real time. It identifies the risk situation through sensor priority measurement and risk situation identification algorithm. If a dangerous situation occurs, a notification message is sent to the guardian indicating the current location of the user. Therefore, it can be expected that if a dangerous situation occurs to a user who needs protection, he can respond promptly.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.15-23
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• 2020

While research activities in the agricultural field for climate change are being actively carried out, smart agriculture using information and communication technology has become a new trend in line with the Fourth Industrial Revolution. Accordingly, research is being conducted to identify and respond to signs of abnormal growth in advance by monitoring the stress of crops in various outdoor environments and soil conditions. There are also attempts to analyze data collected in real time through various sensors using artificial intelligence techniques or big data technologies. In this paper, we propose a big data model that is effective in analyzing the growth environment informations and biometric information of crops by using the existing relational database for big data analysis. The performance of the model was measured by the response time to a query according to the amount of data. As a result, it was confirmed that there is a maximum time reduction effect of 23.8%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.470-473
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• 2021

In this study, we developed the biometric ultrasound transducer, residual thickness measurement algorithm and optimized ultrasound operation methods to diagnose precise conditions of implanted medical prosthetic material inserted during total hip artificial joint replacement. In detail, ultrasound transducers having 8 MHz and 20 MHz center frequencies with similar sensitivity and bandwidth were fabricated to measure various thicknesses of commercial polyethylene-based artificial hip liners, resulting in a comparative analysis of signal-to-noise ratio and axial resolution to conduct an optimization study of ultrasound operations in vivo.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1179-1187
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• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.489-492
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• 2005

Even though the blood pressure is one of the most widely used index for the healthcare monitoring of hypertensive and normotensive persons, there is no non-intrusive measurement method which is commercialized until now. Pulse Arrival Time (PAT) is known that it has close relation with the systolic blood pressure (SBP) and arterial stiffness. In this study, SBP estimation methods by non-intrusive measurement of PAT are suggested. For the unconstrained measurement of PAT, the first method used the electrically non contact electrocardiogram (ENC-ECG) technique and the reflective type of Photoplethysmography (PPG) sensor on the computer mouse. In the second method, ENC-ECG and the air pressure sensor in the seat cushion on a chair were measured. The third method used ECG electrodes and PPG sensors on the toilet seat cover. The validation and regression analysis of the relationship of PAT and SBP are summarized. These methods have considerable errors to be used for all people. But these can be applied for each subject after the parameter customization within acceptable error. So, it is feasible for suggested methods to be used for monitoring of SBP in daily life in non-intrusive way when there is personal identification system of each subject.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.493-496
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• 2005

For home healthcare, the unconstrained measurement of physiological signal is highly required to avoid the inconvenience of users. The recording and analysis of the fundamental parameters during sleep like respiration and heart beat provide valuable information on his/her healthcare. Using the air mattress sensor system, the respiration and heart beat movements can be measured without any harness or sensor on the subject's body. The differential measurement technique between two air cells is adopted to enhance the sensitivity. The balancing tube between two air cells is used to increase the robustness against postural changes during the measurement period. The meaningful frequency range could be selected by the pneumatic filter with balancing tube. ECG (Electrocardiography) and respiration sensor (plethysmography) were measured for comparison with the signal from air mattress. To extract the heart beat information from air pressure sensor, digital signal processing technique was used. The accuracy for breathing interval and heart beat monitoring was acceptable. It shows the potentials of air mattress sensor system to be the unconstrained home sleep monitoring system.