• Journal of the Ergonomics Society of Korea
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• v.9 no.1
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• pp.3-14
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• 1990

This article develops a cost-effective and accurate measurement system for heart best intervals. The system is composed of an analog to digital (A/D) converter, an IBM personal computer (an 8088 microprocessor, an 8253-5 timer, an 8259A interrupt controller, and memories) and assembler programs for controlling these hardware components. An exponential smoothing algorithm effectively reduced noise effects from A/D converted electrocardiogram (ECG) signals influenced by 60 Hz alternating current (AC). The system can collect 15000 heart beat intervals with an 1/5400 second unit.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.13-14
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• 2015

In the past few decades, many catastrophic natural disasters have occurred not only in Japan and Korea, but also in other countries in the world, forcing people to live in unfamiliar houses for middle or long range evacuation periods. Residents staying in temporary houses exhibit insomnia, resulting in severe fatigue. In order to investigate sleeping state of residents, measuring vital signals has been performed at examination room of a hospital. To avoid the restriction of residents' movement, we propose to use smartphone and/or wearable devices with various high performance sensors like measuring heart beat rate. We clarify the availability and usefulness of those devices as support for analyzing daily sleeping state of residents.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.835-836
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• 2009

모니터링은 시스템의 문제 상황을 판단하기 위해서 아주 중요한 요소이다. 응답시간과 같은 외부적인 요인들은 쉽게 모니터링이 가능하지만, 시스템의 동작 상태와 같은 내부의 상태를 모니터링하도록 개발하는 것은 많은 노력이 든다. 본 논문에서는 UML을 이용하여 HB(Heart-Beat) 모니터링할 수 있는 모니터링 코드를 생성하는 방법을 제안한다. 본 방법론에서는 AOP를 이용한 모니터링 코드를 생성하여 기존 시스템의 코드를 수정하지 않고도 모니터링 기능을 추가할 수 있으며, UML을 기반으로 큰 노력 없이 자동으로 모니터링 코드 생성이 가능하다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.794-798
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• 2018

There is a growing need for a care system that can continuously monitor, manage and effectively relieve stress for modern people. In recent years, mobile healthcare devices capable of measuring heart rate have become popular, and many stress monitoring techniques using heart rate variability analysis have been actively proposed and commercialized. In addition, respiratory biofeedback methods are used to provide stress relieving services in environments using mobile healthcare devices. In this case, breathing information should be measured well to assess whether the user is doing well in biofeedback training. In this study, we extracted the heart beat interval signal from the PPG and used the oscillator based notch filter based on the IIR band pass filter to track the strongest frequency in the heart beat interval signal. The respiration signal was then estimated by filtering the heart beat interval signal with this frequency as the center frequency. Experimental results showed that the number of breathing could be measured accurately when the subject was guided to take a deep breath. Also, in the timeing measurement of inspiration and expiration, a time delay of about 1 second occurred. It is expected that this will provide a respiratory biofeedback service that can assess whether or not breathing exercise are performed well.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.38 no.4
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• pp.278-283
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• 2002

The response of electrocardiogram(ECG) of Nile tilapia, Oreochromis niloticus [Linnaeus] was studied to the electric stimulus which was given to a certain part of body The experiments were performed in such a way that three levels of electric stimulus (20, 30, 40 Vp ； 10 msec) were given to fishes with electrode inserted into their bodies and then their ECGs were recorded continuously for 60 minutes in the water temperature of 16~18$^{\circ}C$ The results of the experiments were divided by day and night, and then were analyzed by experimental conditions as follows; 1. Nile tilapia reached a stable condition within 3 minutes after the electrode inserted into their bodies during anesthesia. In stable condition, the heart rates average was 45.8 beat/min during daytime and 45.0 beat/min at night. The action potentials average was 1.76 $mutextrm{V}$during daytime and 1.75 $mutextrm{V}$ at night. 2. The heart rates average by three levels of electric stimulus were \circled1 In the stimulus condition, the heart rates were 34.9 beat/min during daytime and 33.4 beat/min at night for the 20 Vp level, 36.8 bea/min during daytime and 36.0 beat/min at night for the 30 Vp level, and 38.0 beat/min during daytime and 36.4 beat/min at night for the 40Vp level. \circled2 In the recovery condition, the action potentials were 45.5 beat/min during daytime an 45.1 beat/min at night for the 20Vp level, 47.9 beat/min during daytime and 49.0 beat/min at night for the 30Vp level, and 51.4 beat/min during daytime and 50.7 beat/min at night for the 40Vp level 3. The action potentials average by three levels of electric stimulus were, \circled1 In the stimulus condition, action potentials were 2.54 $mutextrm{V}$ during daytime and 2.39 $mutextrm{V}$ at night for the 20 Vp level, 3.30 $mutextrm{V}$ during daytime and 2.30 $mutextrm{V}$ at night for the 30 Vp level and 6.05 $mutextrm{V}$ during daytime and 3.23 $mutextrm{V}$ at night for the 40 Vp level. \circled2 In the recovery condition, action potentials were 1.92 $mutextrm{V}$ during daytime and 1.95 $mutextrm{V}$ at night for the 20 Vp level and 2.78 $mutextrm{V}$ during daytime and 2.21 $mutextrm{V}$ at night for the 30Vp level and 3.6 0 $mutextrm{V}$ during daytime and 2.98 $mutextrm{V}$ at night for the 40 Vp level.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.898-902
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• 2018

A novel heart beat interval estimation algorithm is presented based on parabola approximation method. This paper presented a two-step processing scheme; a first stage is finding R-peak in the Electrocardiogram (ECG) by Shannon energy envelope estimator and a secondary stage is computing the interpolated peak location by parabola approximation. Experimental results show that the proposed algorithm performs better than with the previous method using low sampled ECG signals.

• Journal of the Ergonomics Society of Korea
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• v.2 no.2
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• pp.3-10
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• 1983

While three subjects were running on treadmill at five different speeds, their heart beat interval times were measured and analyzed. From the analysis, we discovered some relation- ships between workload and cardiac response, especially cardiac arrhythmia. Using these relationships, a physioligical model for estimating workload was developed. Although pulse rate has been considered as a good measure of physical load, this study shows that it is highly subject dependent and therefore unsuitable for task evalution. It is recommended to use range of heart beat interval times rather than pulse rate in the evaluation of light work.

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.151-158
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• 2003

Numerous studies of short-term, beat-to-beat variability in cardiovascular signals have used linear analysis techniques. However, no study has been done about the appropriateness of linear techniques or the comparison between linearities and nonlinearities in short-term, beat-to-beat variability. This paper aims to verify the appropriateness of linear techniques by investigating nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average(ARMA) with nonlinear neural network(NN) models for predicting current instantaneous heart rate(HR) and mean arterial blood pressure(BP) from past HRs and BPs. To evaluate these models. we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that 10 technique provides adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.117-128
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• 2006

For the legacy IEEE 802.15.3 WPAN protocol, an unpredictable piconet coordinator(PNC) leaving from a piconet without a proper handoff procedure causes an absence of PNC, and thus the piconet gets collapsed. In addition, several beacons from PNCs in adjacent piconets may be collided on a device(DEV) located between those piconets. This beacon collision eventually makes the DEV leave from the piconet. To remedy these two problems, we here propose an Active Seamless Coordinator Switching(ASCS) scheme and a PNC HB based Beacon Alignment(PHBA) one. In the ASCS scheme, a PNC assigns a number of DEVs as next possible PNCs in sequence for provisioning against the abrupt breakdown of the current active PNC. Each nominated DEV proactively sends a probe frame to confirm the operation status of the active PNC. For the case of no response from the PNC, the nominated DEV tries to become a new PNC immediately. In the second PHBA scheme, each PNC is allow to broadcast a special Heart Beat(HB) frame randomly during a superframe period. When a DEV receives a HB frame from other PNC, it promptly sends the related PNCs a special Hiccup Beat(HCB) frame with the superframe information of its associated PNC. As a result, the HCB frame makes both PNCs align their superframe beginning time in order to yield no more beacon collisions. For these two proposed schemes, we show the performance by simulations. We can confirm the enhancement of throughput for each superframe and average frame transfer delay, since each scheme can reduce the duration of piconet collapse. Finally, it is worth while to note that the proposed schemes can be operated with frames those are permitted in the legacy WPAN standard.