• Journal of Digital Convergence
• /
• v.15 no.1
• /
• pp.381-391
• /
• 2017

Biometric technology is a technology for authenticating a user using the physical or behavioral features of the inherent characteristics of the individual. With the necessity and efficiency of the technology in the fields of finance, security, access control, medical welfare, inspection, and entertainment, the service range has been expanding. Biometrics using biometric information such as fingerprints and faces have been exposed to counterfeit and disguised threats and become a social problem. Recent studies using a bio-signal from the inside of the body other than the bio-information of the external body are being developed. This paper analyzes the recent research and technology of biometric systems using bio-signals, ECG, heart sounds, EEG, and EMG to present the skills needed for the development direction. In the future, utilizing the deep learning to build and analyze database to manage bio-signal based big data for the complex condition of individuals, biometrics technologies suitable for real time environment are expected to be researched.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.122-123
• /
• 2022

최근 들어 인공지능에 대한 발달과 많은 매체들로 인해 사람들의 관심이 증가하고 있다. 또한 GPS 나 Beacon 과 같이 위치 측위 기술이 증가함에 따라 실외 측위 기술이 많이 발달되었고, 실내에서도 사용자의 정확한 위치를 측정할 수 있는 기술들이 발달되고 있다. 본 논문에서는 RNN 알고리즘을 이용하여 비콘을 통해 수집된 사용자의 반복적이고 순차적인 위치정보, 타임스탬프 데이터를 학습시키고 ECG 를 결합하여 사용자 인증을 하여 사용자의 시간별 위치 예측과 이상 징후 탐지 시스템을 제안하고자 한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2011.11a
• /
• pp.9-10
• /
• 2011

• Journal of the Korean Magnetics Society
• /
• v.23 no.4
• /
• pp.130-134
• /
• 2013

The clip-type pulsimeter equipped with a magnetic sensing Hall device and the most popular body sign of the electrocardiogram (ECG) were investigated in order to analyze the pulse wave velocity (PWV). The PWV simultaneously calculated by means of time difference between the maximum peak of ECG pulse wave and the starting point of radial artery pulse wave, and distance difference between the heart position and the radial wrist position. The PWV analyzed from the clinical data was a wider scope of 5~7 m/s with an average value of 6 m/s. By the prediction of blood vessel's elasticity from the analysis of PWV, it may be useful for developing an oriental-western diagnostic medical signal device for a U-health-care system in the future.

• Science of Emotion and Sensibility
• /
• v.22 no.2
• /
• pp.101-108
• /
• 2019

The aim of this study was to verify differences in autonomic responses that are affected by the configurations of one-room type units using an electrocardiogram (ECG). Accordingly, 43 one-room units that were collected randomly were classified into ten different types of floor plan configurations mainly according to the location of the bathroom and kitchen. An ECG was subsequently measured for each plan type and the average ratio of the LF/HF (Power in low frequency range/Power in high frequency range) was calculated to measure the comfort level of each space. The results revealed a significant statistical difference between the average LF/HF ratio between the plan types (p < 0.05) and provided compelling evidence suggesting that the configuration of the plan may affect the quality of one-room space. This approach appears to be effective in counteracting stress that may exacerbate psychological disorders in single person households.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1542-1550
• /
• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.312-315
• /
• 2008

Recently, owing to the development of ubiquitous, RFID and local area wireless communication technology, many studies on the system which can measure biomedical signals are being carried out. In this paper, we have designed and implemented an u-Healthcare system based on sensor network using biomedical signal measurement sensors such as ECG, blood pressure, and heartbeats. The biomedical signals from sensor nodes pass through the gateway and are finally transmitted to a healthcare renter. The acquired biomedical signals are processed in the healthcare center and the analyzed results are transmitted to the patients to improve patients' health using either kinesitherapy or dietary treatment.

• Smart Media Journal
• /
• v.7 no.4
• /
• pp.90-98
• /
• 2018

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.344-346
• /
• 2016

FHIR is a protocol that enables easy data exchange from the time the event occurred in health care settings as a standard for next-generation message exchange of HL7. Create a meaningful message from the ECG and medical equipment, and express the messages generated by standardized FHIR message it will be used in various medical institutions to ensure delivery to EMR, such as hospital information systems can query the results via a smartphone. In addition, it thought to be used in the future to expand SMART on FHIR, Cardiology, Cardiovascular Surgery ward in real-time remote monitoring.

• The Journal of the Korea Contents Association
• /
• v.18 no.2
• /
• pp.370-377
• /
• 2018

Focus level has been important mental state in user study. Cardiac response has been related to focus and less clarified. The study was to determine cardiac parameters for recognizing focus level. The sixty participants were asked to play shooting game designed to control two focus levels. Electrocardiogram was measured during task. The parameters of time domain and frequency domain were determined from ECG. As a result of independent t-test, RRI, SDNN, rMSSD and pNN50 of time domain indicator were statistically significant in recognizing focus level. LF, HF, lnLF and lnHF of frequency domain were observed to be significant indicator. The rule base for recognition has been developed by the combination of RRI, rMSSD and lnHF. The rule base has been verified from another sixty data samples. The recognition accuracy were 95%. This study proposed significant cardiac indicators for recognizing focus level. The results provides objective measurement of focus in user interaction design in the fields of contents industry and service design.