• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.89-95
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• 2015

Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

• ETRI Journal
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• v.42 no.4
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• pp.534-548
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• 2020

This study presents a new architecture for a field programmable analog array (FPAA) for use in low-frequency applications, and a generalized circuit realization method for the implementation of nth-order elliptic filters. The proposed designs of both the FPAA and elliptic filters are based on the operational transconductance amplifier (OTA) used in implementing OTA-C filters for biopotential signal processing. The proposed FPAA architecture has a flexible, expandable structure with direct connections between configurable analog blocks (CABs) that eliminates the use of switches. The generalized elliptic filter circuit realization provides a simplified, direct synthetic method for an OTA-C symmetric balanced structure for even/odd-nth-order low-pass filters (LPFs) and notch filters with minimum number of components, using grounded capacitors. The filters are mapped on the FPAA, and both architectures are validated with simulations in LTspice using 90-nm complementary metal-oxide semiconductor (CMOS) technology. Both proposed FPAA and filters generalized synthetic method achieve simple, flexible, low-power designs for implementation of biopotential signal processing systems.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.235-242
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• 2016

A system for measuring the electrodermal activity (EDA) signal occurring at the sweat glands in the left palm and left finger of the human body was implemented in this study. The EDA measurement system (EDAMS) consisted of an algometer, a biopotential measurement system (BPMS), and a PC. Two experiments were performed to evaluate the function and clinical applicability of EDAMS. First, an experiment was carried out on the linearity of the voltage and the pressure that comprised the output signals of the algometer used for applying a pressure stimulus. Second, the amplitude of the EDA signal acquired from the electrode attached to the left palm or finger was measured while increasing the pressure stimulus of the algometer. When the pressure stimulus of the algometer applied to the left scapula was increased, the amplitude of the EDA signal increased. The amplitude of the EDA signal at the left palm was observed to be greater than that at the left finger. The amplitude of the EDA signal was observed to increase in a relatively linear relation with the intensity of the pressure stimuli. In addition, the latency of the EDA signal acquired from the electrode attached to the left palm or finger was measured while increasing the pressure stimulus of the algometer. When the pressure stimulus of the algometer applied to the left scapula was increased, the latency of the EDA signal decreased. The latency of the EDA signal at the palm was observed to be less than that at the finger. The latency of the EDA signal was observed to decrease nonlinearly with the pressure stimuli.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.181-187
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• 2005

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.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.371-372
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• 2008

Many kinds of electrodes have been developed in various forms and shapes for measurement of bio potential signal. Textile electrode has benefit of collect long tenn data monitoring because of it is non-consciousness, convenient and do not occur skin irritation. However, It is very difficult to acquire available data due to high impedance of electrode and unstable skin-electrode contact which generate motion artifact. Also snap button which usually used as mediator between textile and measurement device cause change of electrical characteristics. In this paper, we inflated textile electrode to stabilize contact and add conductive silver paste between textile and snap button to improve conductance. To compare the performance of two methods, flat or inflated and add conductive paste or not, four types of electrodes are tested on each impedance and SNR by ECG measurement. In result, the first type electrode which flat and non-conductive paste showed the worst performance and the last type electrode which is inflated shape and contain conductive paste show the best performance.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.479-487
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• 2004

Biopotential signals have been used as command in systems using electrical stimulation of motor nerves to restore movement after an injury to the central nervous system (CNS). In order to use the voluntary EMG (electromyography) among the biopotentials as a control signal for the electrical stimulation of the same muscle for CNS injury patients, it is necessary to remove M-wave of having high magnitude from raw data. We designed an optimal filter for removing the M-wave and preserving the voluntary EMG and showed that the optimal filter is eigen filter. We also proved that the previous method using the prediction error filter(PEF) is a suboptimal filtering in the sense of preserving the voluntary EMG. On basis of the data obtained from a model for M-wave and voluntary EMG and from actual CNS injury patients, with false-positive rate analysis, the proposed adaptive filter showed a very promising performance in comparison with previous method.