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Analysis of biosignal using artificial neural networks for human-machine interface
Woochul Nam(남우철) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.4
Robust human-machine interface (HMI) can be realized with accurate estimations on user intention (or motion) using his/her biosignal. A model-based approach is inefficient to study biosignal because the data is affected by numerous factors. Thus, artificial neural networks can be an effective computational tool for HMI. Although neural network-based approach is able to estimate states of HMI users with their biosignals, classical neural networks could show limited performances. If stressful experiments are required to acquire biosignals, it is difficult to obtain sufficient datasets for deep neural networks. Moreover, most biosignals show large variations between HMI users. To resolve these issues, data synthesis approaches can be used because they are able to generate artificial biosignal, which increases the size of the datasets. Furthermore, the performance degradation owing to cross-subject variability can be addressed by using domain adaptation neural networks. This work introduces various methods for data synthesis and domain adaptation for HMI.
Development of Clothing-type Wearable Platform System for Biosignal-Monitoring
( Hyun-seung Cho ),( Jin-hee Yang ),( Joo-hyeon Lee ) 한국감성과학회 2022 춘계학술대회 Vol.2022 No.-
The purpose of this study was to develop a wearable platform system that can detect and acquire human’s biosignals (i.e., heart activity signal, respiration rate, etc.) in a non-restrained, unconscious manner. These detected biosignals are transmitted to a processing device to analyze and monitor the physical status. To achieve this, textile-based heart activity electrodes and a strain gauge sensor for the respiration signal measurement were developed, and their performances in detecting each signal were verified. These sensors were embedded in a chest belt to design a wearable platform that can simultaneously measure heart activity and respiration signals. The sensor part of the chest belt has a dual layer structure to detect high-quality signals. Stretch fabric was used on the outer layer and a respiration sensor was attached to the belt. On the inside layer, a non-stretch fabric was used as the base fabric and a heart activity-sensing electrode, that is capable of taking measurements using a modified lead-II heart activity signal induction method, was embroidered onto the fabric. Subjects were asked to wear the chest belt, and a biosignal processor module was attached to verify the system’s performance while simultaneously acquiring the heart activity and respiration signals. As a result, it was confirmed that the two signals were detected in a stable. It is expected that the biosignal-monitoring wearable platform system developed in this study will be able to effectively analyze and monitor biosignals.
Development of a Chest‑Belt‑Type Biosignal‑Monitoring Wearable Platform System
Joo‑Hyeon Lee,Hyun‑Seung Cho,Jin‑Hee Yang,Sang‑Min Kim,Jeong‑Whan Lee,Hwi‑Kuen Kwak,Je‑Wook Chae 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.4
The purpose of this study was to develop a wearable platform system that can detect and acquire a soldier’s biosignals (i.e., heart activity signal, respiration rate, etc.) in a nonrestrained, unconscious manner. These detected biosignals are transmitted to a processing device to analyze and monitor the soldier’s physical status. To achieve this, textile-based heart activity electrodes and a strain gauge sensor for the respiration signal measurement were developed, and their performances in detecting each signal were verifed. These sensors were embedded in a chest belt to design a wearable platform that can simultaneously measure heart activity and respiration signals. The sensor part of the chest belt has a dual layer structure to detect high-quality signals. Stretch fabric was used on the outer layer and a respiration sensor was attached to the belt. On the inside layer, a non-stretch fabric was used as the base fabric and a heart activity-sensing electrode, that is capable of taking measurements using a modifed lead-II heart activity signal induction method, was embroidered onto the fabric. Subjects were asked to wear the chest belt, and a biosignal processor module was attached to verify the system’s performance while simultaneously acquiring the heart activity and respiration signals. More specifcally, it was confrmed that the two signals were detected in a stable. It is expected that the biosignal-monitoring wearable platform system developed in this study will be able to efectively analyze and monitor soldiers’ biosignals.
박정연(Jeong Yeon Park),박재준(Jae Jun Park),권기환(Ki Hwan Kwon),조남규(Nahm Gyoo Cho),안유민(Yoo Min Ahn),이성환(Seoung Hwan Lee),황승용(Seung Yong Hwang) Korean Society for Precision Engineering 2005 한국정밀공학회지 Vol.22 No.4
In this study, a microchip system fabricated with MEMS technology was developed to detect bioelectrical signals. The developed microchip using the conductivity of gold nanoparticles could detect the biopotential with a high sensitivity. For designing the microchip, simulations were performed to understand the effects of the size and number of nanoparticles, and the sensing width between electrodes on the detection of biosignals. Then, a series of experiment was performed to validate the simulation results and understand the feasibility of the proposed microchip design. Both simulation and experimental results showed that as the sensing width between electrodes increased the conductivity decreased. Also, the conductivity increased as the density of gold nanoparticles increased. In addition, it was found that the conductivity that changes with the nanoparticles density could be approximated by a cumulative normal distribution function. The developed microchip system could effectively apply when a biosignals should be measured with a high sensitivity.
u-Healthcare 환경을 위한 가정용 u-House 게이트웨이의 개발
노동우,유수영,천경우,최진욱 대한의료정보학회 2009 Healthcare Informatics Research Vol.15 No.4
Objective: Ubiquitous healthcare (u-Healthcare) is an emerging paradigm in the healthcare environment. One of the most promising applications for u-Healthcare is the ubiquitous home health monitoring system. This paper addresses two significant challenges in the successful application of the ubiquitous home health monitoring system: the uniform integration of measured biosignal data and easy access to monitored biosignal data. Methods: We used the Medical waveform description Format Encoding Rule (MFER) standard to encode biosignal data. A web-based MFER upload ActiveX control was designed and implemented to transfer MFER files to the central repository server in a near real-time basis. All of the integrated biosignal data were then accessed and managed through the central repository server. Results: We developed a u-House server that can serve as a uniform data transferer to integrate measured biosignal data from u-House homes into the remote central repository server. We developed user-friendly web services that allow users to easily search and view monitored biosignal data. Conclusion: The results of this study suggest that the MFER standard can be easily adapted to u-Healthcare systems and that a web-based ubiquitous home health monitoring system has advantages of ubiquitous access and scalability.
System for Collecting Biosignal Data from Multiple Patient Monitoring Systems
윤덕용,이석훈,김태영,고정길,정우영,박래웅 대한의료정보학회 2017 Healthcare Informatics Research Vol.23 No.4
Objectives: Biosignal data include important physiological information. For that reason, many devices and systems have been developed, but there has not been enough consideration of how to collect and integrate raw data from multiple systems. To overcome this limitation, we have developed a system for collecting and integrating biosignal data from two patient monitoring systems. Methods: We developed an interface to extract biosignal data from Nihon Kohden and Philips monitoring systems. The Nihon Kohden system has a central server for the temporary storage of raw waveform data, which can be requested using the HL7 protocol. However, the Philips system used in our hospital cannot save raw waveform data. Therefore, our system was connected to monitoring devices using the RS232 protocol. After collection, the data were transformed and stored in a unified format. Results: From September 2016 to August 2017, we collected approximately 117 patient-years of waveform data from 1,268 patients in 79 beds of five intensive care units. Because the two systems use the same data storage format, the application software could be run without compatibility issues. Conclusions: Our system collects biosignal data from different systems in a unified format. The data collected by the system can be used to develop algorithms or applications without the need to consider the source of the data.
개인병사의 생존성 증대를 위한 생체신호 모니터링 기법의 설계 및 구현
이승열,박상훈,이춘우,김현준,채제욱,Lee, Seung-Youl,Park, Sang-Hoon,Lee, Choon-Woo,Kim, Hyun-Jun,Chae, Jae-Wook 한국군사과학기술학회 2013 한국군사과학기술학회지 Vol.16 No.6
Recently, personal health monitoring system using intelligent biosensors and equipments has been realized. This system can be adopted for soldier's biosignal monitoring system. In this paper, we propose a soldier biosignal monitoring system using personal biosensors, such as the ECG sensors and accelerometer.
이원섭(Wonsup Lee),박장운(Jangwoon Park),김수진(Sujin Kim),윤성혜(Sunghye Yoon),Xiaopeng Yang,이용태(Yongtae Lee),손준우(Joonwoo Son),김만호(Man Ho Kim),유희천(Heecheon You) 대한인간공학회 2010 大韓人間工學會誌 Vol.29 No.1
An analysis of biosignal and performance data collected during driving has increasingly employed in research to explore a human-vehicle interface design for better safety and comfort. The present study developed a protocol and a system to effectively analyze biosignal and driving perfomlance measurements in various driving conditions. Electrocardiogram (ECG), respiration rate (RR), and skin conductance level (SCL) were selected for biosignal analysis in the study. A data processing and analysis protocol was established based on a comprehensive review of related literature. Then, the established analysis protocol was implemented to a computerized system so that immense data of biosignal and driving performance can be analyzed with ease, efficiency, and effectiveness for an individual and/or a group of individuals of interest. The developed analysis system would be of use to examine the effects of driving conditions to cognitive workload and driving performance.
김선미,윤동환,권순일,Lee So-Ryoung,Kim Kwangsoo,Kim Yong Chul,김동기,Oh Kook-Hwan,주권욱,Lee Hyung-Chul,정철우,김연수,한승석 대한신장학회 2022 Kidney Research and Clinical Practice Vol.41 No.3
Background: Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods: This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results: We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion: This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.