• Fashion & Textile Research Journal
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• v.10 no.5
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• pp.676-682
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• 2008

In this study a guideline of the 3D-fit pattern for the ECG(electrocardiogram) measurement of elderly's u-healthcare clothes was proposed. In the screening test of the ECG measurement band, ECG peak band was observable at the band pressure of 0.20 kPa. By employing a 3D body image, tight-fit 3D patterns were made at two different reduction rates of 21%(pattern 1) and 33%(pattern 2), and corresponding pressure of both of the clothes were 0.25 kPa and 0.54 kPa, respectively. Typical waves of ECG were found in both stationary and moving position. In terms of the subjective evaluation of the u-healthcare clothes when worn, it was confirmed that reduction pattern 1(0.25 kPa) conveyed comfortable clothing pressure and pleasantness, which is very close to the result of screening test of ECG band experiment. As results, it is recommended that reduction rate should be adjusted, so that clothing pressure is about 0.2 kPa for the elderly's comfortable and efficient u-healthcare clothes.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.415-419
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• 2004

As the medical environment field has been facilitated by the rapid development of the information technologies and ubiquitous healthcare service based on network, the exchange of medical information between hospitals become more and more important. However, although most electrocardiograph vendors have provided digital modalities and their ECG data management systems, these were proprietary and not compatible with each other due to multiplicity of vendor specific formats and lack of open ECG data standards. Therefore, we aim to design and develop the ECG management system for archiving, saving, searching, viewing and transferring ECG data and the diagnostic reports. And the system is based on Clinical Document Architecture (CDA) developed by HL7 and DICOM Waveform Standard for the exchange of the structured diagnostic reports and the transfer of ECG data.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.239-245
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• 2004

Electrocardiogram and phonocardiogram acquired from ECG and stethoscope by a non-invasive method are bio-signals which laue been a great role in patients diagnosis and used extensively by doctors. And these are mainly used at the examination room or first-and room of hospital. When the patients are located at some places far away from hospital like a car, an airplane and a vessel under way, there are some difficulties in confirming their cardiac sound Especially the patient of ocean going vessel might not have a medical treatment from the professional doctor at times. In this paper, we developed the digital ECG and stethoscope system based on personal computer which can easily measure the patient's cardiac sound and ECG signal, and this might shorten the appearance of the remote maritime telemedicine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.403-411
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• 2022

A motion-based sports game uses a motion sensor or a camera to exercise based on body movements, and it is possible to obtain exercise effects such as improving physical strength while enjoying the game. In prior works, various studies such as usability evaluation has been conducted on motion-based sports games. However, there has been no discussion about how the exercise effect is exerted on users when experiencing motion-based sports games as individual or team play. This study compared the user's exercise effects by analyzing the user's ECG (Electrocardiogram) sensor and the Kinect sensor's skeletal information using Nintendo Switch game that is played individually and as a team. In this paper, the experimental design and method, the quantitative measurement results based on ECG and Kinect, and the results of the post-test subjective measurement are discussed.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.16.1-16.11
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• 2022

Background: Antimony is used in catalysts, pesticides, brake systems, pharmaceuticals, and synthetic fire retardants in the plastic, paint, and rubber industries. Accumulation of trivalent antimony compounds in the body can cause cardiotoxic effects and increase the risk of electrocardiogram (ECG) abnormalities and sudden death. Antimony exposure can result in action potential prolongation, causing a cardiac repolarization delay, which appears as QTc prolongation and T-wave abnormalities on the ECG. There are no studies on antimony-associated cardiac toxicity in Korea. Case presentation: Accordingly, the present study reports cases of ECG abnormalities in workers handling antimony trisulfide at a company located in the Gyeongsangbuk-do region. Nineteen workers employed at an automobile brake lining manufacturer were exposed to antimony trisulfide dust through thermoforming, grinding, and drilling processes. In 2020, the workers were reported to work 12-hour shifts, 5 days a week. The time-weighted average (TWA) of antimony trisulfide exposure measured in workers was 0.0028 mg/m³. Two workers were excluded from the analysis due to pre-existing medical conditions (cardiovascular disease). Of the remaining 17 workers, ECG abnormalities were found in 41% (seven out of 17: four with QTc prolongation and T-wave abnormalities; two with only T-wave abnormalities; and one with only QTc prolongation). Conclusions: This case report outlines the first few cases in Korea in which potential cardiac toxicity caused by occupational exposure to antimony was identified. However, data regarding cardiac toxicity caused by antimony exposure are still lacking in Korea; thus, additional studies are needed to identify causal relationships.

• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.135-140
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• 2012

As the recent advanced in BIO signal measurement technology, our computing platform is rapidly shifting from desktop PCs to Embedded System. Therefore, In this paper introduces an implementation of the same precision as a hospitan ECG system on the Android. The most important fact of the hospital system is connectivity among the PC such as separate means of communication, we can eliminate the separate means of communication through the Porting Embedded System on Android that can be receive ECG signal directly. We also implementation ECG App on Android that can analyze and show the data result directly.

• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.4
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• pp.300-308
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• 2010

This paper describes the development of a low-power electrocardiogram (ECG) touch sensor intended for the use with two dry metal electrodes. An equivalent ECG extraction circuit model encountered in a ground-free two-electrode configuration is investigated for an optimal sensor read-out circuit design criteria. From the equivalent circuit model, (1) maximum sensor resolution is derived based on the electrode's background thermal noise, which originates from high electrode-skin contact impedance, together with the input referred noise of instrumentation amplifier (IA), (2) 60 Hz electrostatic coupling from mains and motion artifact are also considered to determine minimum requirement of common mode rejection ratio (CMRR) and input impedance of IA. A dedicated ECG read-out front end incorporating chopping scheme is introduced to provide an input referred circuit noise of 1.3 ${\mu}V_{rms}$ over 0.5 Hz ~ 200 Hz, CMRR of IA > 100 dB, sensor resolution of 7 bits, and dissipating only 36 ${\mu}W$. Together with 8 bits synchronous successive approximation register (SAR) ADC, the sensor IC chip is implemented in 0.18 ${\mu}m$ CMOS technology and integrated on a 5 cm $\times$ 8 cm PCB with two copper patterned electrodes. With the help of proposed touch sensor, ECG signal containing QRS complex and P, T waves are successfully extracted by simply touching the electrodes with two thumbs.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.465-473
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• 2017

Electrocardiogram(ECG) signal is one of the unique bio-signals of individuals and is used for personal authentication. The existing studies on personal authentication method using ECG signals show a high detection rate for a small group of candidates, but a low detection rate and increased execution time for a large group of candidates. In this paper, we propose a hierarchical algorithm that extracts fiducial points based on curvature of ECG signals as feature values for grouping candidates ​and identifies candidates using waveform-based comparisons. As a result of experiments on 74 ECG signal records of QT-DB provided by Physionet, the detection rate was about 97% at 3-heartbeat input and about 99% at 5-heartbeat input. The average execution time was 22.4 milliseconds. In conclusion, the proposed method improves the detection rate by the hierarchical personal authentication process, and also shows reduced amount of computation which is plausible in real-time personal authentication usage in the future.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.