• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.432-440
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• 2023

This study introduces an efficient readout circuit designed for two-electrode electrocardiogram (ECG) recording, characterized by its low-noise and low-power consumption attributes. Unlike its three-electrode counterpart, the two-electrode ECG is susceptible to common-mode interference (CMI), causing signal distortion. To counter this, the proposed circuit integrates a common-mode charge pump (CMCP) with a window comparator, allowing for a CMI tolerance of up to 20 V_PP. The CMCP design prevents the activation of electrostatic discharge (ESD) diodes and becomes operational only when CMI surpasses the predetermined range set by the window comparator. This ensures power efficiency and minimizes intermodulation distortion (IMD) arising from switching noise. To maintain ECG signal accuracy, the circuit employs a chopper-stabilized instrumentation amplifier (IA) for low-noise attributes, and to achieve high input impedance, it incorporates a floating high-pass filter (HPF) and a current-feedback instrumentation amplifier (CFIA). This comprehensive design integrates various components, including a QRS peak detector and serial peripheral interface (SPI), into a single 0.18-㎛ CMOS chip occupying 0.54 mm². Experimental evaluations showed a 0.59 µV_RMS noise level within a 1-100 Hz bandwidth and a power draw of 23.83 µW at 1.8 V.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.322-328
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• 2011

There are many ways to detect the heart rate non-invasively such as ECG, PPG, strain gauge, and pressure sensor. In this paper, the pulse wave measurement system using bipolar biased head on mode of the Hall sensor is proposed for measuring the radial artery pulse. TMS320F2812 was used to implement the proposed system and a portable wireless network(zig-bee) was used to show the experimental result. It was confirmed from experiment that the performance of the implemented system was more stable and faster than PPG sensor or piezoelectric film pressure sensor.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.28-37
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• 2007

IEEE 1451 standard defines an interface for network and transducer. In this paper, We propose an architectural model to configure data acquisition system and wireless smart sensor node based on IEEE 1451 standard. Proposed Network Capable Application Processor(NCAP) supports the task of data acquisition and communication for smart sensor node and network. The NCAP is able to reconfigure without interrupting the functionality of the wireless sensor node and receives the critical information of transducer using the DB. Smart sensor node is able to provide the basic information of sensor in digital format. This digital format is called Transducer Electronic Data Sheet(TEDS), is capable of plug-and-play capability of wireless sensor node and the NCAP. We simplify the format of TEDS and template to apply to wireless network environment. information of TEDS and template is transmitted using ad-hoc routing. This study system uses body temperature sensor and ECG(Electrocardiogram) sensor to provide the medical information service. The format of template is selected by data sheet of the sensor and reconfigured to accurately describe the property of the sensor. DB of NCAP is possible to register new template and information of the property as developing new sensor.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1509-1518
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• 2016

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.121-130
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• 2018

We developed multimedia esophageal catheters for use with birds to measure and record ECG and angular velocity while anesthesized, at rest, and in flight. These catheters enable estimates of blood pressure based on readings given by an angular velocity sensor and by RR intervals of ECG affected by EMG. In our experiments, the catheters had the following characteristics: 1. Esophageal catheters offer a topological advantage with 8-dB SNR improvement due to elimination of electromyography (EMG). 2. We observed a very strong correlation between blood pressure and the angular velocity of esophageal catheter axial rotation. 3. The impulse conduction pathway (Purkinje fibers) of the cardiac ventricle has a direction opposite to that of the mammalian pathway. 4. Sympathetic nerves predominate in flight, and RR interval variations are strongly suppressed. The electrophysiological data obtained by this study provided especially the state of the avian autonomic nervous system activity, so we can suspect individual's health condition. If the change of the RR interval was small, we can perform an isolation or screening from the group that prevent the pandemics of avian influenza. This catheter shall be useful to analysis an avian autonomic system, to perform a screening, and to make a positive policy against the massive infected avian influenza.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.135-143
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• 2012

Arterial stiffness is causing the serious problems for human who is suffered from hypertension and metabolic syndrome. So it is important that measure the arterial stiffness for early prevention. Many researches point out that pulse wave velocity(PWV) is the reliable and simple method to predict arterial stiffness. In this paper, we developed the sensing parts that detect the pulse wave and ECG by using piezoelectric film and conductive textile with elastic band. Our system could detect 3ch pulse wave and ECG. Simultaneously, our algorithm extracts the features for calculating the delays among pulse waves. The delays are the significant parameter to estimate PWV, thus we design the experiment for evaluating the performance of our sensing parts. The reference is PP-1000(HanByul Meditech, Korea) that is good for performance evaluation. As a result, the start point of the pulse wave was the most reliable feature for comparing with PP-1000(r=0.691, P=0.00). The results between two operators showed that there is only a slight difference in the reproducibility of the devices. In conclusion, we assume that the suggested sensor could be more comfortable and faithful method for arterial stiffness.

• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.340-344
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• 2018

Electrodes are an important part of electrocardiography (ECG); disposable electrodes have been extensively used. However, personal ECG monitoring devices for Internet of Things applications require reusable electrodes. As there have been no systematic studies on the characteristics of reusable electrodes to date, we conducted this study to assess the performance and feasibility of electrodes with different materials. We built reusable electrodes using twelve different metallic materials, including commonly used copper, silver, zinc, plating materials, chemically inert titanium, stainless steel, and aluminum. Each electrode was fabricated to a size of $5{\times}10mm$. Their characteristics such as offset, baseline drift, stabilization time, and chemical inertness were compared. A personal ECG monitoring system was used to test the manufactured electrodes. The performances of the Ag, Cu, and Zn electrodes were better than the performances of other electrodes. However, these materials may not be used owing to the chemical changes that occur when the electrodes are in contact with the skin, such as discoloration and corrosion, which deteriorate their electrical characteristics. Titanium, stainless steel, and aluminum are chemically stable. The titanium electrode showed the best performance among the three, and it is our recommendation as a material for manufacturing reusable electrodes.