• Journal of the Ergonomics Society of Korea
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• v.29 no.1
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• pp.113-120
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• 2010

This research is to evaluate the arousal level by using cardiovascular response. PPG was used in this study as one of the method of measuring it rather than ECG (Electrocardiography) for the purpose of solving ergonomic problem of sensing. The participants were in the age group of 20 (mean=24, standard deviation=1.25): five men and five women. Each experiment composed with four identical sets. First, a black screen was displayed for 30 second rest. Then, the prepared 6 pair images were randomly presented for 10 second stimulation and for 30 second non-stimulation. PPG was measured on the earlobes of experimenters at 200Hz sampling frequency. PPG amplitude, PPI(Pulse to Pulse Interval), and PRV(Pulse Rate Variability) were analyzed according to arousal level. T-test was performed to compare between the PPG variables of rest and relaxation, rest and arousal, and relaxation and arousal. Relative to the rest state, PPG amplitude decreased in relaxed state and increased in aroused state. Relative to the rest state, PPI decreased in both emotional states. However, more significant decline was observed in aroused state. PRV's LF and HF were used in the form of LF/HF to compare between the relaxed and the aroused state. Therefore, PPG signal showed significant differences between relaxed and aroused state. In conclusion, evaluation of human arousal level used in the PPG analysis demonstrated that PPG has better usability and comforter measurement than ECG and is clearly an alternative method of measuring arousal level.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.1-10
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• 2020

In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.

• The Journal of the Convergence on Culture Technology
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• v.3 no.2
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• pp.55-59
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• 2017

We are confirmed to the transfer degree of physical signal with the communication code on the wireless communication system. The physical transfer system was consisted of such as ECG, PPG and Bluetooth part that received signal. Communication method was take the international standard level of IEEE802.15.1 forms, the frequency transition was needed the bandwidth of communication for transfer signal with the band-communication method. The program was expressed to receive with wireless communication condition that was consisted of such as the serial chart, processing and app inventor. Their signal was identified to transfer certainly the corrected signal. Therefore, signal processing for coding by the real-time graphing and using processing by a standard capacity of serial-chart graphing that will be possible to progress the improvement effectiveness of wireless communication system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1252-1256
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• 2016

Internet of Things(IoT)-devices are now expanding inter-connecting networking technologies to invent healthcare monitoring system especially for assessing physiological conditions of the chronically-ill patients those with cardiovascular diseases. Hence, IoT system is expected to be utilized for home healthcare by dedicating the original usage of IoT devices to collect the biomedical data such as electrocardiogram(ECG) and photoplethysmography(PPG) signal. The aim of this work is to implement health monitoring system by integrating IoT devices with Raspberry-pi components to measure and analyze ECG and the multi-channel PPG signals. The acquired data and fiducial features from our system can be transmitted to mobile devices via wireless networking technology to support the concept of tele-monitoring services based on IoT devices.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.926-932
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• 2009

The whole-body massager among healthcare devices is under being popularized in a large scale as times goes by. It is critical to measure, analyze and judge the stress relaxsation trend from HRV signal using PPG in case of massager operation with such relaxsation device for removing stress of human being. There may be artifact in HRV measured signal because the measured object is under shaking with that massager in this case. We present the methodology to remove such artifact from those measured HRV signal, and then measure and analyze the desired HRV successfully in this paper.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.197-202
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• 2010

In this paper, multi-channel vital signal monitoring system was implemented for home healthcare. The system able to measure vital signal for example ECG, PPG and temperature simultaneously at patients’ home. The vital signal is an essential parameter for healthcare application and can be easily extracted from patients. The implemented system consist of sensor parts for signal extraction, signal amplifier and filter for analog circuit, analog signal to digital conversion for controlling devices and lastly the monitoring program. The system able to transmit vital signals using Bluetooth wireless communications to personal computer or home server. And the tele-monitoring system able to display real-time signals using web monitoring program. In medical application, the vital signal parameter able to stored and saved in the web server for further medical analysis. This system opens up the possibilities of ubiquitous healthcare where further implementation can be easily done.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.195-200
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• 2016

With development of wearable devices, there is an increased interest in bio-signal monitoring techniques that can measure the user's health condition. However, embedding several bio-signal sensors in one wearable device has some inherent problems in terms of limited resources such as its size. Furthermore, such problem also arise when new bio-signal sensors are added. In this paper, we introduced the Bio-Cradle, which is a Plug-in module that can transfer the biological signals in real time from the accelerometer, ECG, or PPG sensor to other wearable devices at the request from the user of wearable devices. When the Bio-Cradle plugged in to the other device, it can transfer several synchronized bio-signals regardless of the type of device.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.352-358
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• 2012

A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawback is its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoring system in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since it is important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Down sampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increase compression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electrocardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photoplethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared to other previous reports.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.267-268
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• 2002

We developed nausea, caused by disorder of autonomic nervous system, detection system using bio-signal analysis and artificial neural network in virtual reality enironment. We used 16 bio-signals, 9 EEGs, EOG, ECG, SKT, PPG, GSR, RSP, EGC, which has own analysis methods. We estimated nausea level by artificial neural network.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.637-644
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• 2008

Human emotion has been tried to be recognized by physiological measurements in developing emotion machine enabling to understand and react to user's emotion. This study is to find the time-dependent physiological measurements and their variation characteristics for discriminating emotions according to dimensional emotion model. Ten university students were asked to watch sixteen prepared images to evoke different emotions. Their subjective emotions and autonomic nervous responses such as ECG (electrocardiogram), PPG (photoplethysmogram), GSR (Galvanic skin response), RSP (respiration), and SKT(skin temperature) were measured during experiment. And these responses were analyzed into HR(Heart Rate), Respiration Rate, GSR amplitude average, SKT amplitude average, PPG amplitude, and PTT(Pulse Transition Time). TDPs(Time dependent parameters) defined as the delay, the activation, the half recovery and the full recovery of respective physiological signal in this study have been determined and statistically compared between variations from different emotions. The significant tendencies in TDP were shown between emotions. Therefore, TDP may provide useful measurements with emotion recognition.