• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.176-179
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• 2001

A portable bio-signal measurement system for 24-hours continuous health monitoring of the elderly and the disabled is presented. The measurement system has the functions of acquisition of various bio-signals such as ECG, EMG and EEG, wireless data transmission/receive and adjustment of parameters such as gain and cut-off frequency. The data is sent to a host computer or other device via a Bluetooth. The design targets of the developing system for volume and power consumption are $20{\times}30{\times}5(mm^3)$ and 8mW.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.137-142
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• 2005

Biomedical signals such as ECG, EMG, and EEG are extremely important in the diagnosis of patients. It is difficult to filter noise from these signals, and errors resulting from filtering can distort a biomedical signal. Existing systems have shown poor performance when complicated noise appears. Adaptive filtering is selected to contend with these defects. Existing adaptive filters can adjust the filter coefficient with the given filter order, but they can produce an unsuitable order in different environments. In order to solve this problem, an optimal adaptive filter with a dynamic structure was designed. Positive experimental results were obtained.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.887-888
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• 2006

In this paper, we devised mac-yule detection system which provide resting state mac-yule. The devised system composed of signal transformation part, signal processing part, and PC based display part. Hardware part consisit of PPG, ECG, EEG, EMG, and RSP. Also, software system consist of bio-signal processing software which detecting mac-yule. EEG-$\alpha$, $\beta$ wave analysis algorithm that use wavelet transformation, RSP detecting algorithm which used zero-crossing method.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.207-214
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• 2010

The aim of this study was to investigate upper and lower limb muscle activity using EMG(electromyogram) sensors while walking and identify normal gait pattern using FSR(force sensing resistor) sensor. Fifteen college students participated in this study and their EMG and FSR signal were measured during stopping and walking trials. EMG signals from upper(pectoralis major and trapezius) and lower limbs(rectus femoris, biceps femoris, vastus medialis, vastus lateralis, semimembranosus, semitendinosus, soleus, peroneus longus, gastrocnemius medialis, and gastrocnemius lateralis) were obtained using the surface electrodes. FSR measured pressures on 8 areas of the sole of the foot during walking. EMG results showed that all muscle activities except for vastus lateralis and semimembranosus during walking had higher amplitudes than stopping. Additionally, muscle activities associated with stance and swing phase during walking were identified. Results on FSR showed that stance and swing phases were detected by FSR signals during a gait cycle. Eight gait phases－initial contact, loading response, mid stance, terminal stance, pre swing, initial swing, mid swing, and terminal swing- were classified.

• 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.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1641-1648
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• 2017

Many researchers had examined the validity of using the high-to-low ratio between two fixed frequency band amplitudes (H/L-FFB) from the surface electromyography of a face and body as the first spectral index to assess muscle fatigue. Despite these studies, the disadvantage of this index is the lack of a criterion for choosing the optimal border frequency. We tested the potential of using the high-to-low ratio between two signal spectral moments (H/L-SSM), without fixed border frequencies, to evaluate muscle fatigue and predict endurance time ($T_{end}$), which was determined when the subject was exhausted and could no longer follow the fixed contraction cycle. Ten healthy participants performed five sets of voluntary isotonic contractions until they could only produce 10% and 20% of their maximum voluntary contraction (MVC). The $T_{end}$ values for all participants were $138{\pm}35s$ at 10% MVC and $69{\pm}20s$ at 20% MVC. Changes in conventional spectral indices, such as the mean power frequency (MPF), Dimitrov spectral index (DSI), H/L-FFB, and H/L-SSM, were extracted from surface EMG signals and were monitored using the initial slope computed every 10% of $T_{end}$ as a statistical indicator and compared as a predictor of $T_{end}$. Significant correlations were found between $T_{end}$ and the initial H/L-SSM slope as computed over 30% of $T_{end}$. In conclusion, initial H/L-SSM slope can be used to describe changes in the spectral content of surface EMG signals and can be employed as a good predictor of $T_{end}$ compared to that of conventional spectral indices.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.713-716
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• 1987

A new approach to the real-time measurement of muscle fatigue by using adaptive filtering algorithm is proposed. Unlike previously reported methods, it can estimate the muscle fatigue at every sample as the EMG signal statistics change. As a result, the muscle conduction velocity ranged between 4.2-5m/s at low tension and 3-4m/s at fatigue state.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.281-284
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• 2002

대부분의 보조기 및 의족은 기계식이 주류이며 환자가 착용시 정상보행은 부드럽지 못하고 원활한 보행을 위해서는 장기간 동안 훈련이 요구된다. 따라서 대퇴의족에서 발생하는 이러한 문제점을 개선하기 위해 본 연구에서는 대퇴절단자(transfermoral amputee)의 보행을 정상보행에 일치하는 보행 능력을 복원하기 위해 근전도 신호와 인공신경망을 이용하여 적응 PID제어기를 설계하였고 그 제어 결과를 제시하였다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1074-1080
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• 1988

A new approach to the measurement of muscle fatigue by adaptive digital filtering algorithm is proposed. Unlike previously reported methods, it can estimate the muscle fatigue at every sample as the EMG signal statistics change. As a result, the muscle conduction velocity ranged between 4.2-5m/s at low tension and 3-4m/s at fatigue state.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.259-263
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• 1996

Psychological action is physiological response of outernal stimulus. Physiological response is accompanied b physiological signals which are EEG, EMG, GSR, ECG, BP, and tec. Physiological signals are recently studied for determination of human phychological state. Psychological activity causes electric potential of brain. Physiological signal is considered as measurement of human psychological state. Aditory sensibility which is one of the sense of human may determine differences between positive and negative feeling. EEG and GSR variation with auditory quality of stimulus can be define human negative and positive mental state. This study is to characterize parameters which can determine negative and positive psycholigical state of human.