• Journal of Computing Science and Engineering
• /
• v.7 no.4
• /
• pp.272-284
• /
• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.188-192
• /
• 1997

The purpose of this study are changing wired medical instrument's terminal into wireless and implementing BSS of Wireless Local Area Network. the wireless terminal using frequency hopping spread spectrum in ISM band transfers patients medical information data such as ECG data, Patient Disease Indication Message to AP(or Server) and it also performs that as a response of transmission request in server. we made Clinet-Server network structure support only BSS service and patient's terminal controlled by polling in server. Wireless Terminal will guarantee mobility and give doctors real time monitoring capability in office.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.893-895
• /
• 2016

세계적으로 헬스케어 산업의 발전 가능성은 눈에 띄게 증가하고 있다. 그 중에서도 환자 혹은 각종 디바이스 사용자의 생체신호를 다루는 기술은 다양한 중요정보를 얻을 수 있다. 본 논문에서는 심전도의 미세한 생체 전위를 측정하기 위해 각종 필터와 증폭기를 이용하여 회로를 설계하고 이를 Cortex-M3 Microprocessor와 MATLAB 프로그램을 이용하여 필터링과 데이터통신을 통해 최종적으로 실시간으로 모니터링 하였다. 일반적으로 임상이나 진단에 이용되는 ECG 신호는 각종 심장질환의 지표로 사용되지만 전문적인 지식을 갖추지 않은 일반 사용자가 사용하기에는 어려운 점이 없지 않아 있다. 따라서 이 연구는 아날로그 신호를 디지털 신호로 변환하여 생체신호를 다루는 다양한 분야에서 용이할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2015.07a
• /
• pp.101-102
• /
• 2015

본 논문에서는 다수의 생체계측 정보를 MDEC(Medical Device Exchange Communication) 기반의 공통모듈을 이용하여 중앙 집중 관리를 위한 CMS(Central Monitoring System)를 제안한다. 기존의 바이탈사인 모니터 등은 환자별로 부착되어 다수의 환자에 대한 이상 징후를 실시간으로 모니터링하기에는 어려움이 있다. 이에 본 논문에서는 다수의 계측 장비를 통합하는 공통모듈인 MDEC을 이용하여 집중 모니터링이 가능한 CMS을 구현하였다. 계측장비와 MDEC의 패킷 송수신을 위한 프로토콜을 설계하고 네트워크로 전송된 패킷은 CMS에 표시되며 데이터베이스에 저장된다. 본 연구의 결과는 급속히 증가하는 병원내 환자 관리 및 효율적 의료서비스에 기여할 것으로 사료된다.

• Journal of Yeungnam Medical Science
• /
• v.41 no.1
• /
• pp.53-55
• /
• 2024

A noncontact sensor field is an innovative device that can detect, measure, or monitor physical properties or conditions without direct physical contact with the subject or object under examination. These sensors use a variety of methods, including electromagnetic, optical, and acoustic technique, to collect information about the target without physical interaction. Noncontact sensors find wide-ranging applications in various fields such as manufacturing, robotics, automobiles, security, environmental monitoring, space industry, agriculture, and entertainment. In particular, they are used in the medical field, where they provide continuous monitoring of patient conditions and offer opportunities in rehabilitation medicine. This article introduces the potential of noncontact sensors in the field of rehabilitation medicine.

• Journal of Satellite, Information and Communications
• /
• v.11 no.3
• /
• pp.18-23
• /
• 2016

Internet of things (IoT) is revolutionizing in the patient-Centered medical monitoring and management by authorizing the Smartphone application and data analysis with medical centers. The network connectivity is basic requirement to collect the observed human beings' health information from Smartphone to monitor the health from IoT medical devices in personal healthcare. The IoT environment built in Smartphone is very effective and does not demand infrastructure. This paper presents the smart phone deployed personal IoT architecture for Non-Invasive ECG Capturing. The adaptable IoT medical device cum Gateway is used for personal healthcare with big data storage on cloud configuration. In this approach, the Smartphone camera based imaging technique used to extract the personal ECG waveform and forward it to the cloud based big data storage connectivity using IoT architecture. Elaborated algorithm allows for efficient ECG registration directly from face image captured from Smartphone or Tablet camera. The profound technique may have an exceptional value in monitoring personal healthcare after adequate enhancements are introduced.

• Korean Journal of Clinical Pharmacy
• /
• v.25 no.3
• /
• pp.138-144
• /
• 2015

Objective: It is to evaluate the drug interaction monitoring program as a pilot project to develop a pharmaceutical care model in a medical intensive care unit and to analyze the influencing factors of drug interactions. Method: Electronic medical records were retrospectively investigated for 116 patients who had been hospitalized in a medical intensive care unit from October to December in 2014. The prevalence of adverse reaction with risk rating higher than 'D' was investigated by Lexi-$Comp^{(R)}$ Online database. The factors related with potential drug interaction and with treatment outcomes were analyzed. Results: The number of patients with a potential interaction of drug combination was 92 (79.3%). Average ages, the length of stay in the intensive care unit and the numbers of prescription drugs showed significant differences between drug interaction group and non-drug interaction group. Opioids (14.4%), antibiotics (7.2%), and diuretics (7.2%) were most responsible drug classes for drug interactions and the individual medications included furosemide (6.4%), tramadol (4.9%), and remifentanil (4.5%). There were 950 cases with a risk rating of 'C' (84.6%), 142 cases with a risk rating of 'D' (12.6%), and 31 cases with a risk rating of 'X' (avoid combination) (2.8%). The factors affecting drug interactions were the number of drugs prescribed (p < 0.0001) and the length of stay at intensive care unit (p < 0.01). The patients in intensive care unit showed a high incidence of adverse reactions related to potential drug interaction. Therefore, drug interaction monitoring program as a one of pharmaceutical care services was successfully piloted and it showed to prevent adverse reaction and to improve therapeutic outcomes. Conclusion: Active participation of a pharmacist in the drug management at the intensive care unit should be considered.

• Health Policy and Management
• /
• v.28 no.4
• /
• pp.402-410
• /
• 2018

Background: Monitoring appropriate medication categories can provide early warning of certain disease outbreaks. This study aimed to present a methodology for selecting and monitoring medications relevant to the surveillance of acute respiratory tract infections, such as influenza. Methods: To estimate correlations between acute febrile respiratory tract infection and some medication categories, the cross-correlation coefficient (CCC) was used and established. Two databases were used: real-time prescription trend of antivirals, anti-inflammatory drugs, antibiotics using Drug Utilization Review Program between 2012 and 2015 and physicians' number of encounters with acute febrile respiratory tract infections such as influenza outbreaks using the national level health insurance claims data. The seasonality was also evaluated using the CCC. Results: After selecting six candidate diseases that require extensive monitoring, influenza with highly specific medical treatment according to the health insurance claims data and its medications were chosen as final candidates based on a data-driven approach. Antiviral medications and influenza were significantly correlated. Conclusion: An annual correlation was observed between influenza and antiviral medications, anti-inflammatory drugs. Suitable models should be established for syndromic surveillance of influenza.

• Journal of Biomedical Engineering Research
• /
• v.42 no.4
• /
• pp.175-185
• /
• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.32 no.1
• /
• pp.78-88
• /
• 2022

Objectives: To develop the smart sensor to protect worker's health from chemical exposure by adopting ICT (Information and Communications Technology) technologies. Methods: To develope real-time chemical exposure monitoring system, IoT (Internet of Things) sensor technology and regulations were reviewed. We developed and produced smart sensor. A smart sensor is a system consisting of a sensor unit, a communication unit, and a platform. To verify the performance of smart sensors, each sensor has been certified by the Korea Laboratory Accreditation Scheme (KOLAS). Results: Chemicals (TVOC; Total Volatile Organic Compounds, Cl₂: Chlorine, HF: Hydrogen fluoride and HCN: Hydrogen cyanide) were selected according to a priority logic (KOSHA Alert, acute poisoning statistics, literature review). Notifications were set according to OEL (occupational exposure limit). Sensors were selected based on OEL and the capabilities of the sensors. Communication is designed to use LTE (Long Term Evolution) and Wi-Fi at the same time for convenience. Electronic platform were applied to build this monitoring system. Conclusions: Real-time monitoring system for OEL of hazardous chemicals in workplace was developed. Smart sensor can detect chemicals to complement monitoring of traditional workplace environmental monitoring such as short term and peak exposure. Further research is needed to expand the scope of application, improve reliability, and systematically application.