• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.397-407
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• 1997

In this paper, we present a signal analysis environment with visual programming capability, which is called Signal Analysis and Visualization Environment( SAVE). The system allows a user to perform visually programmed analysis of signals and visualize the results. The visualization facility enables the user to compare original signals with processed ones arid also to display signals overlaid synchronously with the events extracted from the signals. The SAVE system has an extensible architecture: each signal processing algorithm is implemented as a separate building block object module, which can be freely added or removed from the SAVE system without any code modification. We describe the overall structure of the SAVE system and the building block objects, which provide the extensibility in collaboration with together. We illustrate some test runs in order to five a taste of how to use and where to use the system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.932-933
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• 2014

최근에는 의료 빅데이터 분야에서 의료기기, 의료전문가로부터 생성 또는 감지되는 사운드 생체신호(심장박동, 호흡, 맥박, 진맥) 데이터의 특징을 디지털 데이터로 추출하여 패턴 데이터로 변환한 후, 이를 빅데이터 분석 플랫폼 기반으로 분석하여 진료, 처방, 예방 등에 유용한 정보를 생성하는 모델 구축 연구가 활성화되고 있다. 본 논문에서는 사운드 생체신호 특징을 디지털 데이터로 추출하여 (주)리아컴즈 NeoQubit 빅데이터 플렛폼을 기반으로 패턴 데이터를 분석하고 예측할 수 있는 모델을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.181-183
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• 2003

최근 대용량의 데이터베이스로부터 유용한 정보를 발견하고 데이터간에 존재하는 연관성을 탐색하고 분석하는 데이터 마이닝에 관한 많은 연구들이 진행되고 있다. 다양한 생체 신호를 분석하기 위하여 데이터 마이닝 기법을 이용할 수 있다. 본 논문에서는 심전도 신호의 패턴을 분류하기 위하여 신경망 기법을 적용하였다. 최근 패턴분류에 있어서 각광을 받고 있는 SVM 모델은 학습과정에서 얻어진 확률분포를 이용하여 의사결정함수를 추정한 후 이 함수에 따라 새로운 데이터를 이원분류 하는 것으로 분류 문제에 있어서 일반화 기능이 매우 높다. 기존에 많이 이용되던 BP 모델과 비교평가 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.357-360
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• 2008

Vital sign monitoring system using IEEE 502.IS.4 based wireless sensor network(WSN) is designed and developed on mobile environment and sensor node platform. WSN and CDMA are integrated to create a wide coverage to support various environments like inside and outside. We developed query processor to use selective any devices(ECG, Blood pressure and sugar module) and control of the self-organizing network of sensor nodes in a wireless sensor network. Vital sign from wireless medical any devices are analysed in cell phone first for real time signal analyses and the abnormal vital signs are sent and save to hospital server for detail signal processing. wireless signal traffic in wireless sensor network environment or data communication inside the cell phone is reduced.

• Journal of the HCI Society of Korea
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• v.3 no.1
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• pp.1-7
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• 2008

Recently researchers have studied mobile physiological sensing device. However, previous works focused on multiple and real time physiological sensing method, instead of aesthetic shape of sensing devices, sensing comfort during monitoring and sensing reliability against the hand motion artifact. In this work, we propose a stone shaped physiological sensing device to monitor the physiological status in a daily life which maximize the aesthetic feeling and sensing comfort and sensing reliability. We proposed stepwise user centered design process for user centric physiological sensing device and evaluated appropriate sensing positions against the hand motion artifacts and pressure from sensors. From the usability test and experiments, we verified the proposed sensing device provides the aesthetic appeals, sensing comfort and sensing reliability. We expect that this work can be applied in the various health care applications in near future.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

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

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.173-180
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• 2000

In this paper, we interpreted and evaluated the relation between the sensation of equilibrium and biomedical signal automatically by applying the fuzzy theory. We induced the vertigo by using the caloric test, and presented the correlation between vertigo and biomedical signal by using the quantification method. We objectively analyzed the organic relation of the biomedical signal by fuzzy rule design using the table-lookup scheme and obtained good result in recognizing the level of the sensation of equilibrium.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.252-253
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• 2012

최근 들어 정신적/신체적 질병을 치료하기 위해 음악을 많이 사용한다. 음악 치료의 수요가 늘어남에 따라 HCI, BCI, Music Therapy 등과 같은 음악의 성질과 생체신호간의 관계, 심리상태 변화 등에 관한 연구들이 많이 이뤄지고 있다. 또한 압축기술의 발달로 인하여 디지털 음원을 손쉽게 듣고 다닐 수 있으며, 생체 신호처리 이론, 생체 신호처리 장비 등이 발달함에 따라 디지털 음원을 이용하여 음악 치료 연구를 할 수 있게 되었다. 하지만 이러한 디지털 음원이 음악치료 측면에서는 효과가 크지 않을 뿐만 아니라 오히려 악영향을 미친다는 내용이 방송/신문을 통해 기사화 되고 있다. 따라서 본 논문에서는 동일한 음원의 아날로그/디지털 음악으로 청감테스트를 진행하고, 생체신호(혈압, 심박수, 뇌파)를 측정하여 차이점을 비교/분석한다.