• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.211-220
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• 2019

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.689-694
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• 2011

Objective: The aim of this study is to evaluate the fluctuation of signal amplitude during repetitive dynamic contraction based on surface electromyography(EMG). Background: The most previous studies were considered isometric muscle contraction and they were difference to smoothing window length by moving average filter. In practical, the human movement is dynamic state. Dynamic EMG signal which indicated as the nonstationary pattern should be analyzed differently compared with the static EMG signal. Method: Ten male subjects participated in this experiment, and EMG signal was recorded by biceps brachii, anterior/posterior deltoid, and upper/lower trapezius muscles. The subject was performed to repetitive right horizontal lifting task during ten cycles. This study was considered three independent variables(muscle, amplitude processing technique, and smoothing window length) as the within-subject experimental design. This study was estimated muscular activation by means of the linear envelope technique(LE). The dependent variable was set coefficient of variation(CV) of LE for each cycle. Results: The ANOVA results showed that the main and interaction effects between the amplitude processing technique and smoothing window length were significant difference. The CV value of peak LE was higher than mean LE. According to increase the smoothing window length, this study shows that the CV trend of peak LE was decreased. However, the CV of mean LE was analyzed constant fluctuation trend regardless of the smoothing window length. Conclusion: Based on these results, we expected that using the mean LE and 300ms window length increased reproducibility and signal noise ratio during repetitive dynamic muscle contraction. Application: These results can be used to provide fundamental information for repetitive dynamic EMG signal processing.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.694-698
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• 1987

In this paper, the primitive and double combined motion classification of the arm is discussed using pattern recognition of EM signal. The EM signals are detected from Ag-Ag/Cl surface electrodes, and IBM PC, calculated the Likelyhood probability and the decision function on the feature space of integral absolute value. Multiclass decision rule is introduced for higher decision rate. On our experimental results from expert simulator, the decision rate of more than 78% can be obtained.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.708-716
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• 2003

The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional electrical stimulation. To use the voluntary EMG signal, it is necessary to eliminate the muscle response evoked by the electrical stimulation and enable to process the algorithm in real time. In this paper, we propose the Gram-Schmidt(GS) algorithm and implement it in FPGA(field programmable gate array). GS algorithm is efficient to eliminate periodic signals like muscle response, and is more stable and suitable to FPGA implementations than the conventional least-square approach, due to the systolic array structure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.2
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• pp.43-48
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• 2012

In this paper, we present the method of gait phases detection using multi biomedical signals during normal gait. Electromyogram(EMG) signals, muscle of thigh angle measurement device and resistive sensors are used for experiments. We implemented a test targeting five adult male and identified the pattern of EMG signal of normal gait. For acquiring the EMG signal, subjects attached surface Ag/AgCl electrodes to quadriceps femoris, biceps femoris, tibialis anterior and gastrocnemius medialis. Resistance sensors are attached to the heel toe and soles of the each feet for measuring attachment state of between feet and ground. Infrared sensors are attached on the thigh and thigh angle measurement device has the range from flection 25 degrees to extension 20 degrees. The results of this paper, The stance and swing phase could be confirmed during the normal gait and be classified in detail the eight steps.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.135-140
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• 2012

Previous investigators have shown that the frequency compression is related to the muscle fatigue and the decreasing conduction velocity of muscle fibers. The aim of the present study was to investigate the relationship between endurance times and frequency parameters such as mean power frequency and median frequency in the surface EMG signal during isotonic contractions. Eight healthy subjects performed voluntary isotonic contractions of biceps Brachii muscle until their endurance times which were determined when the subject could no longer follow the contraction cycle. The regressive slopes of mean power frequency and median frequency were used to describe the frequency compression of the surface EMG signal, and to test the predictability of endurance time. As results of experiment, significant correlations were found between endurance time and the regressive slopes of mean power frequency and mean frequency computed over 50%Tend of endurance time.

• 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%).

• Physical Therapy Korea
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• v.10 no.1
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• pp.37-50
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• 2003

The purpose of this study was to compare visual analogue scale (VAS), pain threshold (PT), $%RMS_{RVC}$, and EMG gaps before and after applying transcutaneous electrical nerve stimulation (TENS) on the upper trapezius muscle at the patients with myofascial pain syndrome (MPS). The subjects were 4 men and 10 women composed of both the inpatients and outpatients who were diagnosed as MPS at Wonju Medical Center. VAS and PT measurements were performed to assess the subjective pain level. The reference voluntary contraction (RVC) test was performed for 15 seconds for normalization on the bilateral trapezius muscle using surface electromyography (sEMG). After 3-minute resting time, the EMG signal was recorded while performing a typing activity for 2 minutes and then TENS was applicated with a comfortable intensity for 10 minutes. The EMG activity of the upper trapezius muscle was recorded during typing for 2 minutes. The results of study were as follows: 1) VAS score was significantly decreased on the more painful side after treatment, however, it was not significantly different on the less painful side. 2) PT was increased after treatment on both sides, however, it was not significantly different between before and after the TENS application. 3) The EMG activity during typing was significantly decreased after treatment, and 4) The EMG gaps were significantly increased after TENS treatment compared to before it. Consequently, the study showed that TENS was effective in decreasing VAS, $%RMS_{RVC}$, and in increasing EMG gaps. The EMG gap analysis could be a useful method to measure pain in patients with MPS in the upper trapezius.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.88-94
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• 2003

Electromyogram (EMG) is the bioelectric signal induced by motor nerves. Analyzing EMG with the movement produced by muscle contraction, we can provide input commands to a computer as a man-machine interface as well as can evaluate the patient's motional abnormality. In this paper, we developed an integrated miniaturized device which acquires and transmits the surface EMG of an interested muscle. Developed system measures $60{\times}40{\times}25mm$, weighs 100g. Using an amplifier circuitry on the electrodes and the radio frequency transmission, the developed system dispenses with the use of cables among the electrodes, amplifier, and the post processing system (personal computer). The wiring used in conventional systems can be obstacle for natural motion and source of motion artifacts. In results, the developed system improves not only the signal-to-noise ration in dynamic EMG measurement, but also the user convenience. We propose a new human-computer interface as well as a dynamic EMG measurement system as a possible application of the developed system.