• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.89-96
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• 2017

This study developed a method which recognizes emotional interactions from the social distance by a beacon wearable device. The recognized interaction was evaluated by comparing with the cardiovascular synchrony from photoplethysmogram (PPG). The interaction was recognized when social distance was maintained for a certain period of time. The cardiovascular synchrony was estimated by correlation anaysis between beat per minute (BPM) from PPG. The maintenance time was determined by Mann-Whitney U test between the cardiovascular synchrony of interaction and non-interaction groups. Fifteen groups (2 persons per a group) participated in the experiment and they were asked to wear the beacon and PPG wearable devices in daily life. Experimental results showed that the interaction groups had more higher cardiovascular synchrony than non-interaction groups and the significant interaction time was determined to be 11 seconds (p=.045). Consequently, the real-time measurement and evaluation of the social network in real space was expected to be improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.16-22
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• 2010

Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

• Journal of Acupuncture Research
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• v.19 no.2
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• pp.14-27
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• 2002

Objective : The aim of this study is to reveal the meaning of Shi-Dong-Bing and Suo-Sheng-Bing through investigating the origin of Shi-Dong-Bing and Suo-Sheng-Bing. Methods : We analyzed and compared the meridian symptoms of "ju Bi Shi Yi Mai Jiu Jing, "Ju Bi", "Yin Yang Shi Yi Mai Jiu Jing" and "Lin Shu Jing Mai". Results : Suo-Sheng-Bing seems to have been originated from the meridian symptoms of "Ju Bi" and Shi-Dong-Bing is different from the meridian symptoms of "Ju Bi". therefore two meridian symptoms differ in the source of formation and they seems to be different concerning recognition system for disease. Conclusion : Shi-Dong-Bing is the meridian symptoms, in case of feeling abnormal beat by pulse diagnosis, and this pulse diagnosis method is comparative pulse diagnosis method that compare all the pulse point of every meridians. Suo-Sheng-Bing seems to be the meridian symptoms describing the disease of somatic surface with making reference to meridian-circulating positions, afterward have been increased to the related internal organ's disease.