• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.191-198
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• 2020

In this study, we proposed a real-time ECG analytical method for predicting stress and dangerous heart condition using the ECG signal in playing AR/VR device. A real-time diagnosis is used as R-R interval based HRV(:Heart rate variability), BPM(:Beats Per Minitue) and autonomic nervous research with through mapping method of two-dimensional planes. The ECG data were analyzed every 5 minutes and derived from autonomic nervous system diagnosis.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.1
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• pp.24-28
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• 1995

Exercise physiology of fish was studied by means of Electro-cardio-gram(ECG) technique with wired electrode system. Effects of swimming activity on the heart rate change for carp Cyprinus carpio was observed and analysed under swimming speeds among 1~3 Body Length/s and swimming durations of 10 and 60 minutes in the flume tank. The heart rate increase during swimming activity was observed in higher speed and longer duration conditions. The exercise effect on the heart rate continued even after fish stopped swimming. The time for recovery after exercise was tended to be elongated with the higher exercise condition.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.105-114
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• 2015

We propose a real time user interface that utilizes emotion recognition by physiological signals. To improve the problem that was low accuracy of emotion recognition through the traditional EEG(ElectroEncephaloGram), We developed a physiological signals-based emotion recognition system mixing relative power spectrum values of theta/alpha/beta/gamma EEG waves and autonomic nerve signal ratio of ECG (ElectroCardioGram). We propose both a data map and weight value modification algorithm to recognize six emotions of happy, fear, sad, joy, anger, and hatred. The datamap that stores the user-specific probability value is created and the algorithm updates the weighting to improve the accuracy of emotion recognition corresponding to each EEG channel. Also, as we compared the results of the EEG/ECG bio-singal complex data and single data consisting of EEG, the accuracy went up 23.77%. The proposed interface system with high accuracy will be utillized as a useful interface for controlling the game spaces and smart spaces.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.33-37
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• 2006

In this paper, we present a centralized monitoring system for infant incubator using Bluetooth. Conventional monitoring systems for incubators require large space and wire connection, which causes the spatial restrictions. To overcome this disadvantage, centralized monitoring system is proposed for infant incubators using Bluetooth. The implemented system consists mainly of transmission systems and receiver systems. There are temperature sensors, humidity sensors, ECG measurement units and Bluetooth modules in the transmission systems. For temperature, humidity and ECG data, the transmission systems acquire them from the measuring modules in the incubator and transmits the signals using Bluetooth. In the receiver system, users can see the status of the infant by accessing the central monitoring host computer. That is, one can monitor the information on the temperature and the humidity in the incubator and Infant's ECG without dependence to a conventional bulky system. Also, the system manager in the receiver system can maintain centralized monitoring of the situations in all incubators and infant. The developed system will be useful in remote diagnosis of infant incubator In various environments.