• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.51-58
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• 2013

As it is changing into aging society, rehabilitation, welfare and sports industry markets are being expanded fast. Especially, the field of vital signals interface to control welfare instruments like wheelchair, rehabilitation ones like an artificial arm and leg and general electronic ones is a new technology field in the future. Also, this technology can help not only the handicapped, the old and the weak and the rehabilitation patients but also the general public in various application field. The commercial bio-signal measurement instruments and interface systems are complicated, expensive and large-scaled. So, there are a lot of limitations for using in real life with ease. this thesis proposes a wireless transmission interface system that uses EMG(electromyogram) signals and a control module to manipulate hardware systems with portable size. We have designed a hardware module that receives the EMG signals occurring at the time of wrist movement and eliminated noises with filter and amplified the signals effectively. DSP(Digital Signal Processor) chip of TMS320F2808 which was supplied from TI company was used for converting into digital signals from measured EMG signals and digital filtering. We also have used PCA(Principal Component Analysis) technique and classified into four motions which have right, left, up and down direction. This data was transmitted by wireless module in order to display at PC monitor. As a result, the developed system obtains recognition success ratio above 85% for four different motions. If the recognition ratio will be increased with more experiments. this implemented system using EMG wrist direction signals could be used to control various hardware systems.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.471-478
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• 2004

Electromyogram (EMC) signal generated by voluntary contraction of muscles is often used in a rehabilitation devices such as an upper limb prosthesis because of its distinct output characteristics compared to other bio-signals. This paper proposes an EMG-based human-computer interface (HCI) for the control of the above-elbow prosthesis or the wheelchair. To control such rehabilitation devices, user generates four commands by combining voluntary contraction of two different muscles such as levator scapulae muscles and flexor-extensor carpi ulnaris muscles. The muscle contraction is detected by comparing the mean absolute value of the EMG signal with a preset threshold value. However. since the time difference in muscle firing can occur when the patient tries simultaneous co-contraction of two muscles, it is difficult to determine whether the patient's intention is co-contraction. Hence, the use of the comparison method using a single threshold value is not feasible for recognizing such co-contraction motion. Here, we propose a novel method using double threshold values composed of a primary threshold and an auxiliary threshold. Using the double threshold method, the co-contraction state is easily detected, and diverse interface commands can be used for the EMG-based HCI. The experimental results with real-time EMG processing showed that the double threshold method is feasible for the EMG-based HCI to control the myoelectric prosthetic hand and the powered wheelchair.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.21-24
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• 2002

We proposed the intelligent system to control prosthetic robot. The proposed intelligent system was used competitive network, SOFM and LVQ, and consisted of pre-processing part and associative part. A pre-processing part was processed EMG signal and associative part was outputted signal to control prosthetic robot. To verify the effectiveness, we adapted to 2 link manipulator for korean consonant.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.48-57
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• 2003

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) with genetic algorithm and hidden Markov models (HMM's) hybrid classifier. Genetic Algorithms play a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrast, the multilayer feedforward networks are suitable for static patterns. And, a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of ANN and HMM algorithms that might lead to further improved EMG recognition systems.

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.227-233
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• 2014

In this paper, to quantitatively evaluate the degree of rehabilitation for the disabled of unilateral lower extremity, we compared the EMG pattern of normal and simulated abnormal gait. The EMG signal was measured at a rate of 1 kHz on the quadriceps and biceps femoris, the pressure sensor was attached to the sole in order to distinguish the gait cycle. Integrated EMG (IEMG) was obtained by the gait cycle, and classified four patterns that were the normal gait pattern, amplitude decrease pattern, reversed pattern, and irregular pattern. For comparison of the patterns, a curve fitting was performed using the trigonometric functions. The result of curve fitting, the method using a variable A that corresponds to the amplitude of the regression curve was able to distinguish the reverse pattern and remaining pattern. The coefficient of determination ($R^2$) representing coincidence of the pattern of the regression curve and EMG was confirmed the biggest value at the normal gait. Therefore, the degree of normal gait can be confirmed using the coefficient of determination. This results show that it is possible to quantitatively confirm the degree of unilateral lower extremity disabled rehabilitation, and it will be contributed to the study of efficient rehabilitation methods by objective analysis.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.7
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• pp.40-47
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• 1998

This paper presents the physiological signal processing CMOS one chip for transmitting human bodys small electrical signals such as electrocardiogram(EKG) or electromyogram(EMG) and the external system for receiving signals was implemented by the commercial ICs. For simultaneous four-subject four-channel telemetry, a new enfored synchronization techniqeu using infrared bi-directional communication has been proposed. The telemeter IC with the size of 5.1*5.1mm$^{2}$ has the following functions: receiving of command signal, initialization of internal state of all functional blocks, decoding of subject-selection signal, time multiplexing of 4-channel modulated physiological signals, transmitting of telemetry signal to external system and auto power down control. The newly designed synchronized oscillator with low supply voltage dependence in the telemeter IC operates at a supply voltage from 4.6~6.0V and the nonlinearity error of PIM modulator was less than 1.2%F.S(full scale). The power saving block operates at the period of 2.5ms even if the telemetry IC does not receive command signal from external system for a constant time.

• Korean Journal of Applied Biomechanics
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• v.15 no.1
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• pp.177-195
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• 2005

The purpose of this study was to analyze the kinematic variables of ankle joints and EMG signal of the lower limbs muscle activity for the different walking speed. The subjects were 6 males of twenties. It was classified into three different walking speed-0.75m/s, 1.25m/s, 1.75m/s. The walking performances were filmed by high speed video camera and EMG signal was gained by ME3000P8 Measurement Unit. Tibialis anterior(TA), Gastrocnemius medial head(GM), Gastrocnemius lateral head(GL), Ssoleus(SO) were selected for the dorsiflexion and plantarflexion of the ankle joint. The result of this study were as follows: 1. In the gait cycle, The time parameters for the phases were showed significant difference without the terminal stance phase and terminal swing phase for the different walking speed. 2. The angle of ankle joint was no significant difference for each time point and MDF, MPF but increasing walking speed the angle had the increasing pattern slightly. 3. The angular velocity of ankle joint was showed the significant difference for LHC, RTO, RKC, LHU, MPF and MDF point along the walking speed. 4. TA was showed about 2-3 times muscle activity at the 1.75m/s than 1.25m/s in some phases. And it was showed the similar muscle activity between the 0.75m/s and 1.25m/s but, showed a little much muscle activity in the 0.75m/s. GM was showed about 2-3 times muscle activity in the 1.75m/s than 1.25m/s, and even much muscle activity at the 0.75m/s than 1.25m/s in some phases. GL was showed increasing pattern of muscle activity specially in the initial swing phase as the walking speed increased. SO was showed about 3 times muscle activity in the 1.75m/s than 1.25m/s during the plantarflexion of ankle joint. It was showed the similar muscle activity between the 0.75m/s and 1.25m/s but, showed a little much muscle activity in the 1.25m/s.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.5
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• pp.367-372
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• 2013

In this paper, micro dried bio-potential electrodes are demonstrated for sEMG (surface ElectroMyoGraphic) signal measurement using conductive epoxy on the textile fabric. Micro dried bio-potential electrodes on the textile fabric substrate have several advantages over the conventional wet/dry electrodes such as good feeling of wearing, possibility of extended-wearing due to the good ventilation. Also these electrodes on the textile fabric can easily apply to the curved skin surface. These electrodes are fabricated by the screen-printing process with the size of $1mm{\times}10mm$ and the resultant resistance of these electrodes have the average value of $0.4{\Omega}$. The conventional silver chloride electrode shows the average value of $0.3{\Omega}$. However, the electrode on the textile fabric are able to measure the sEMG signal without feeling of difference and this electrode shows the lower resistance of $1.03{\Omega}$ than conventional silver chloride electrode with $2.8{\Omega}$ in the condition of the very sharp curve surface (the radius of curvature is 40 mm).

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.377-382
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• 2004

In the present study, thirteen healthy volunteers performed lumbar extension exercises at 48$^{\circ}$/s, loaded by 40, 50, 60kg(about 44, 55, 66％ of maximum voluntary contraction). During the whole period of exercises, electromyographic(EMG) signal was measured in the erector spinae muscle in order to determine muscle fatigue. Using the wavelet transform, EMG signal was separated by various frequency ranges in the time-frequency domain, and muscle fatigue was analyzed, comparing with the results based on the median frequency(MDF). MDF shifted toward the lower frequency ranges with the muscle fatigue, showing a single characteristic frequency. On the other hand, wavelet transform of EMG signals resulted in increased power amplitude in lower frequency ranges(0-125Hz), and decreased power amplitude in higher frequency ranges(375-468Hz). This study reveals that the muscle fatigue during dynamic movement is explained better by wavelet analysis.