Transactions on Electrical and Electronic Materials
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v.17
no.3
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pp.150-154
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2016
Contact electrodes pose threats like inflammation, metal poisoning, and allergic reaction to the user during long term ECG procedure. Therefore, we present a novel noncontact electrocardiographic electrode designed through microelectromechanical systems (MEMS) process. The proposed ECG electrode consists of small inner and large outer circular copper plates separated by thin insulator. The inner plate enables capacitive transduction of bio-potential variations on a subject’s chest into a voltage that can be processed by a signal processing board, whereas the outer plate shields the inner plate from environmental electromagnetic noise. The electrode lead wires are also coaxially designed to prevent cables from coupling to ground or electronic devices. A prototype ECG electrode has an area of about 2.324 cm2, is very flexible and does not require power to operate. The prototype ECG electrode could measure ECG at about 500 um distance from the subject’s chest.
Lee Myung-Joon;Chu Jun-Uk;Ryu Je-Cheong;Mun Mu-Seong;Moon Inhyuk
Journal of Institute of Control, Robotics and Systems
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v.11
no.1
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pp.41-49
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2005
Electromyogram (EMG) signal generated by voluntary contraction of muscles is often used in rehabilitation devices because of its distinct output characteristics compared to other bio-signals. This paper proposes a novel EMG-based human-computer interface for electric-powered wheelchair users with motor disabilities by C4 or C5 spine cord injury. User's commands to control the electric-powered wheelchair are represented by shoulder elevation motions, which are recognized by comparing EMG signals acquired from the levator scapulae muscles with a preset double threshold value. The interface commands for controlling the electric-powered wheelchair consist of combinations of left-, right- and both-shoulders elevation motions. To achieve a real-time interface, we implement an EMG processing hardware composed of analog amplifiers, filters, a mean absolute value circuit and a high-speed microprocessor. The experimental results using an implemented real-time hardware and an electric-powered wheelchair showed that the EMG-based human-computer interface is feasible for the users with severe motor disabilities.
When designing image sensors including a CMOS vision chip for edge detection, resolution is a significant factor to evaluate the performance. It is hard to improve the resolution of a bio-inspired CMOS vision using a resistive network because the vision chip contains many circuits such as a resistive network and several signal processing circuits as well as photocircuits of general image sensors such as CMOS image sensor (CIS). Low resolution restricts the use of the application systems. In this paper, we improve the resolution through layout and circuit optimization. Furthermore, we have designed a printed circuit board using FPGA which controls the vision chip. The vision chip for edge detection has been designed and fabricated by using $0.35{\mu}m$ double-poly four-metal CMOS technology, and its output characteristics have been investigated.
ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches' results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.
Journal of the Korea Society of Computer and Information
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v.28
no.10
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pp.133-153
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2023
In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.
Proceedings of the Korean Society for Agricultural Machinery Conference
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2017.04a
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pp.156-156
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2017
Most crop damages have been occurred by vermin(e.g., wild birds and herbivores) during the period between seeding and the cotyledon level. In this study, to minimize the damage by vermin and acquire the benefits such as protection against weeds and maintenance of water content in soil, immediately vinyl mulching after seeding was devised. Vinyl mulching has been generally covered with black color vinyl, that crop seeding locations cannot be detected by visible light range. Before punching vinyl, non-contact and non-destructive methods that can continuously determine the locations are necessary. In this study, a crop position detection method was studied that uses infrared thermal image sensor to determine the cotyledon position under vinyl mulch. The moving system for acquiring image arrays has been developed for continuously detecting crop locations under plastic mulching on the field. A sliding mechanical device was developed to move the sensor, which were arranged in the form of a linear array, perpendicular to the array using a micro-controller integrated with a stepping motor. The experiments were conducted while moving 4.00 cm/s speed of the IR sensor by the rotational speed of the stepping motor based on a digital pulse width modulation signal from the micro-controller. The acquired images were calibrated with the spatial image correlation. The collected data were processed using moving averaging on interpolation to determine the frame where the variance was the smallest in resolution units of 1.02 cm. For this study, the spline method was relatively faster than the other polynomial interpolation methods, because it has a lower maximum order of formulation when using a system such as the tridiagonal linear equation system which provided the capability of real-time processing. The temperature distribution corresponding to the distance between the crops was 10 cm, and the more clearly the leaf pattern of the crop was visually confirmed. The frequency difference was decreased, as the number of overlapped pixels was increased. Also the wave pattern of points where the crops were recognized were reduced.
Purpose: Pulse crop damage due to wild birds is a serious problem, to the extent that the rate of damage during the period of time between seeding and the stage of cotyledon reaches 45.4% on average. This study investigated a method of fundamentally blocking birds from eating crops by conducting vinyl mulching after seeding and identifying the growing locations for beans to perform punching. Methods: Infrared (IR) sensors that could measure the temperature without contact were used to recognize the locations of soybean cotyledons below vinyl mulch. To expand the measurable range, 10 IR sensors were arranged in a linear array. A sliding mechanical device was used to reconstruct the two-dimensional spatial variance information of targets. Spatial interpolation was applied to the two-dimensional temperature distribution information measured in real time to improve the resolution of the bean coleoptile locations. The temperature distributions above the vinyl mulch for five species of soybeans over a period of six days from the appearance of the cotyledon stage were analyzed. Results: During the experimental period, cases where bean cotyledons did and did not come into contact with the bottom of the vinyl mulch were both observed, and depended on the degree of growth of the bean cotyledons. Although the locations of bean cotyledons could be estimated through temperature distribution analyses in cases where they came into contact with the bottom of the vinyl mulch, this estimation showed somewhat large errors according to the time that had passed after the cotyledon stage. The detection results were similar for similar types of crops. Thus, this method could be applied to crops with similar growth patterns. According to the results of 360 experiments that were conducted (five species of bean ${\times}$ six days ${\times}$ four speed levels ${\times}$ three repetitions), the location detection performance had an accuracy of 36.9%, and the range of location errors was 0-4.9 cm (RMSE = 3.1 cm). During a period of 3-5 days after the cotyledon stage, the location detection performance had an accuracy of 59% (RMSE = 3.9 cm). Conclusions: In the present study, to fundamentally solve the problem of damage to beans from birds in the early stage after seeding, a working method was proposed in which punching is carried out after seeding, thereby breaking away from the existing method in which seeding is carried out after punching. Methods for the accurate detection of soybean growing locations were studied to allow punching to promote the continuous growth of soybeans that had reached the cotyledon stage. Through experiments using multiple IR sensors and a sliding mechanical device, it was found that the locations of the crop could be partially identified 3-5 days after reaching the cotyledon stage regardless of the kind of pulse crop. It can be concluded that additional studies of robust detection methods considering environmental factors and factors for crop growth are necessary.
Journal of the Institute of Electronics and Information Engineers
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v.54
no.5
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pp.24-32
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2017
In this paper, a low power 4th order delta-sigma modulator was designed with a high resolution of 12 bits or more for the biological signal processing. Using time-interleaving technique, 4th order delta-sigma modulator was designed with one operational amplifier. So power consumption can be reduced to 1/4 than a conventional structure. To operate stably in the big difference between the two capacitor for kT/C noise and chip size, the variable-stage amplifier was designed. In the first phase and second phase, the operational amplifier is operating in a 2-stage. In the third and fourth phase, the operational amplifier is operating in a 1-stage. This was significantly improved the stability of the modulator because the phase margin exists within 60~90deg. The proposed delta-sigma modulator is designed in a standard $0.18{\mu}m$ CMOS n-well 1 poly 6 Metal technology and dissipates the power of $354{\mu}W$ with supply voltage of 1.8V. The ENOB of 11.8bit and SNDR of 72.8dB at 250Hz input frequency and 256kHz sampling frequency. From measurement results FOM1 is calculated to 49.6pJ/step and FOM2 is calculated to 154.5dB.
Recently, the bio-healthcare market is enlarging worldwide due to various reasons such as the COVID-19 pandemic. Among them, biometric measurement and analysis technology are expected to bring about future technological innovation and socio-economic ripple effect. Existing systems require a large-capacity battery to drive signal processing, wireless transmission part, and an operating system in the process. However, due to the limitation of the battery capacity, it causes a spatio-temporal limitation on the use of the device. This limitation can act as a cause for the disconnection of data required for the user's health care monitoring, so it is one of the major obstacles of the health care device. In this study, we report the concept of a standalone healthcare monitoring module, which is based on both triboelectric effects and electromagnetic effects, by converting biomechanical energy into suitable electric energy. The proposed system can be operated independently without an external power source. In particular, the wireless foot pressure measurement monitoring system, which is rationally designed triboelectric sensor (TES), can recognize the user's walking habits through foot pressure measurement. By applying the triboelectric effects to the contact-separation behavior that occurs during walking, an effective foot pressure sensor was made, the performance of the sensor was verified through an electrical output signal according to the pressure, and its dynamic behavior is measured through a signal processing circuit using a capacitor. In addition, the biomechanical energy dissipated during walking is harvested as electrical energy by using the electromagnetic induction effect to be used as a power source for wireless transmission and signal processing. Therefore, the proposed system has a great potential to reduce the inconvenience of charging caused by limited battery capacity and to overcome the problem of data disconnection.
KIPS Transactions on Computer and Communication Systems
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v.6
no.6
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pp.271-280
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2017
In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.
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