• Title/Summary/Keyword: Biometric Signal processing

Search Result 19, Processing Time 0.025 seconds

An Implementation of User Identification System Using Hrbrid Biomitic Distances (복합 생체 척도 거리를 이용한 사용자 인증시스템의 구현)

  • 주동현;김두영
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.3 no.2
    • /
    • pp.23-29
    • /
    • 2002
  • In this paper we proposed the user identification system using hybrid biometric information and non-contact IC card to improve the accuracy of the system. The hybrid biometric information consists of the face image, the iris image, and the 4-digit voice password of user. And the non-contact IC card provides the base information of user If the distance between the sample hybrid biometric Information corresponding to the base information of user and the measured biometric information is less than the given threshold value, the identification is accepted. Otherwise it is rejected. Through the result of experimentation, this paper shows that the proposed method has better identification rate than the conventional identification method.

  • PDF

Method of Biological Information Analysis Based-on Object Contextual (대상객체 맥락 기반 생체정보 분석방법)

  • Kim, Kyung-jun;Kim, Ju-yeon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2022.05a
    • /
    • pp.41-43
    • /
    • 2022
  • In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

  • PDF

Bio-Sensing Convergence Big Data Computing Architecture (바이오센싱 융합 빅데이터 컴퓨팅 아키텍처)

  • Ko, Myung-Sook;Lee, Tae-Gyu
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.7 no.2
    • /
    • pp.43-50
    • /
    • 2018
  • Biometric information computing is greatly influencing both a computing system and Big-data system based on the bio-information system that combines bio-signal sensors and bio-information processing. Unlike conventional data formats such as text, images, and videos, biometric information is represented by text-based values that give meaning to a bio-signal, important event moments are stored in an image format, a complex data format such as a video format is constructed for data prediction and analysis through time series analysis. Such a complex data structure may be separately requested by text, image, video format depending on characteristics of data required by individual biometric information application services, or may request complex data formats simultaneously depending on the situation. Since previous bio-information processing computing systems depend on conventional computing component, computing structure, and data processing method, they have many inefficiencies in terms of data processing performance, transmission capability, storage efficiency, and system safety. In this study, we propose an improved biosensing converged big data computing architecture to build a platform that supports biometric information processing computing effectively. The proposed architecture effectively supports data storage and transmission efficiency, computing performance, and system stability. And, it can lay the foundation for system implementation and biometric information service optimization optimized for future biometric information computing.

The Implementation of Wireless Bio-signal Monitoring System for U - healthcare (유비쿼터스 헬스케어를 위한 무선 생체신호 감시 시스템 설계)

  • Lee, Seok-Hee;Ryu, Geun-Taek
    • 전자공학회논문지 IE
    • /
    • v.49 no.2
    • /
    • pp.82-88
    • /
    • 2012
  • In this paper, using the Android-based mobile platform designed and integrated U-healthcare systems for personal health care system is proposed. Integrated Biometric systems, electrocardiogram (ECG), oxygen saturation, blood pressure, respiration, body temperature, such as measuring vital signs throughout the module and signal processing biometric information through wireless communication module based on the Android mobile platform is transmitted to the gateway. Biometric data transmitted from a mobile health monitoring system, or transmitted to the server of U-healthcare was designed. By implementing vital signs monitoring system has been measured in vivo by monitoring data to determine current health status of caregivers had the advantage of being able to guarantee mobility respectively. This system is designed as personal health management and monitoring system for emergency patients will be helpful in the development looks U-healthcare system.

Research trends on Biometric information change and emotion classification in relation to various external stimulus (다양한 외부 자극에 따른 생체 정보 변화와 감정 분류 연구 동향)

  • Kim, Ki-Hwan;Lee, Hoon-Jae;Lee, Young Sil;Kim, Tae Yong
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.20 no.1
    • /
    • pp.24-30
    • /
    • 2019
  • Modern people argue that mental health care is necessary because of various factors such as unstable income and conflict with others. Recently, equipments capable of measuring electrocardiogram (ECG) in wearable equipment have been widely used. In the case of overseas, it can be seen as a medical assistant [14]. By using such functions, studies are being conducted to distinguish representative emotions (joy, sadness, anger, etc.) with objective values. However, most studies are increasing accuracy by collecting complex bio-signals in a limited environment. Therefore, we examine the factors that have the greatest influence on the change and discrimination of biometric information on each stimulus.

A 3 ~ 5 GHz CMOS UWB Radar Chip for Surveillance and Biometric Applications

  • Lee, Seung-Jun;Ha, Jong-Ok;Jung, Seung-Hwan;Yoo, Hyun-Jin;Chun, Young-Hoon;Kim, Wan-Sik;Lee, Noh-Bok;Eo, Yun-Seong
    • JSTS:Journal of Semiconductor Technology and Science
    • /
    • v.11 no.4
    • /
    • pp.238-246
    • /
    • 2011
  • A 3-5 GHz UWB radar chip in 0.13 ${\mu}m$ CMOS process is presented in this paper. The UWB radar transceiver for surveillance and biometric applications adopts the equivalent time sampling architecture and 4-channel time interleaved samplers to relax the impractical sampling frequency and enhance the overall scanning time. The RF front end (RFFE) includes the wideband LNA and 4-way RF power splitter, and the analog signal processing part consists of the high speed track & hold (T&H) / sample & hold (S&H) and integrator. The interleaved timing clocks are generated using a delay locked loop. The UWB transmitter employs the digitally synthesized topology. The measured NF of RFFE is 9.5 dB in 3-5 GHz. And DLL timing resolution is 50 ps. The measured spectrum of UWB transmitter shows the center frequency within 3-5 GHz satisfying the FCC spectrum mask. The power consumption of receiver and transmitter are 106.5 mW and 57 mW at 1.5 V supply, respectively.

Factors affecting real-time evaluation of muscle function in smart rehab systems

  • Hyunwoo Joe;Hyunsuk Kim;Seung-Jun Lee;Tae Sung Park;Myung-Jun Shin;Lee Hooman;Daesub Yoon;Woojin Kim
    • ETRI Journal
    • /
    • v.45 no.4
    • /
    • pp.603-614
    • /
    • 2023
  • Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross-reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real-time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.

The Unconstrained Sleep Monitoring System for Home Healthcare using Air Mattress and Digital Signal Processing (공기 매트리스와 디지털 신호처리를 이용한 홈헬스케어용 무구속 수면 모니터링 시스템)

  • Chee, Young-Joon;Park, Kwang-Suk
    • Proceedings of the IEEK Conference
    • /
    • 2005.11a
    • /
    • pp.493-496
    • /
    • 2005
  • For home healthcare, the unconstrained measurement of physiological signal is highly required to avoid the inconvenience of users. The recording and analysis of the fundamental parameters during sleep like respiration and heart beat provide valuable information on his/her healthcare. Using the air mattress sensor system, the respiration and heart beat movements can be measured without any harness or sensor on the subject's body. The differential measurement technique between two air cells is adopted to enhance the sensitivity. The balancing tube between two air cells is used to increase the robustness against postural changes during the measurement period. The meaningful frequency range could be selected by the pneumatic filter with balancing tube. ECG (Electrocardiography) and respiration sensor (plethysmography) were measured for comparison with the signal from air mattress. To extract the heart beat information from air pressure sensor, digital signal processing technique was used. The accuracy for breathing interval and heart beat monitoring was acceptable. It shows the potentials of air mattress sensor system to be the unconstrained home sleep monitoring system.

  • PDF

Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement Learning, and Q-Learning

  • Park, Ji Su;Park, Jong Hyuk
    • Journal of Information Processing Systems
    • /
    • v.16 no.5
    • /
    • pp.1001-1007
    • /
    • 2020
  • In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

Analysis of Technology and Research Trends in Biomedical Devices for Measuring EEG during Driving (운전 중 EEG 측정을 위한 생체의료기기의 기술 및 연구동향 분석)

  • Gyunhen Lee;Young-Jin Jung
    • Journal of the Korean Society of Radiology
    • /
    • v.17 no.7
    • /
    • pp.1179-1187
    • /
    • 2023
  • Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.