• 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 Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.769-776
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• 2007

This paper proposes the human arm motion tracking algorithm based on the signal processing for surface EMG (electromyogram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the flexion and extension motions of elbow and shoulder. Also, we show the validity of the suggested algorithms through the experiments.

• 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.

• Korean Journal of Acupuncture
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• v.29 no.4
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• pp.634-642
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• 2012

Objectives : The aim of the study is to find out whether effect of acupuncture is depending on the diameter of needle, which is a possible component of dose of acupuncture needling. Methods : To compare acupuncture effects in different diameters of needle, we measured the changes in muscle fatigue recovery using surface electromyogram(sEMG) in healthy 8 volunteers. Muscle fatigue was induced by 20 times sit-up for 1 min. Immediately after induction of muscle fatigue, acupuncture needle was inserted into ST36 or ST25 for 10 min by diameters of 0.20 mm, 0.30 mm, or 0.40 mm needles. The sEMG recording was followed by acupuncture for 30 min. As a control group, sEMG was recorded for the same period at rest after muscle fatigue induction. Results : In both of ST 36 and ST 25, stimulation with 0.4 mm diameter needle showed significant rapid recovery followed by short period of muscle fatigue increase. Stimulation with 0.2 mm diameter significantly suppressed the increase of muscle fatigue. Conclusions : These data suggest that acupuncture effect is, at least in part, dependent on diameter of needle. Therefore, diameter of needle is also considered to achieve effective outcome of acupuncture.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.37-44
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• 2005

Gait control capacity for most trans-femoral prostheses is significantly different from that of a normal person, and training is required for a long period of time in order for a patient to walk properly. People become easily tired when wearing a prosthesis or orthosis for a long period typically because the gait angle cannot be smoothly adjusted during wearing. Therefore, to improve the gait control problems of a trans-femoral prosthesis, the proper gait angle is estimated through surface EMG(electromyogram) signals on a normal leg, then the gait posture which the trans-femoral prosthesis should take is calculated in the neural network, which learns the gait kinetics on the basis of the normal leg's gait angle. Based on this predicted angle, a postural control method is proposed and tested adaptively following the patient's gait habit based on the predicted angle. In this study, the gait angle prediction showed accuracy of over $97\%$, and the posture control capacity of over $90\%$.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.132-141
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• 2009

In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses. Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In addition, we discussed performance of force estimation results related to the length of the muscles. This work may prove useful in relaying natural and delicate commands to artificial devices that may be attached to the human body or deployed remotely.

• Journal of the HCI Society of Korea
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• v.10 no.2
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• pp.21-26
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• 2015

NUI (Natural User Interface) system interprets the user's natural movement or the signals from human body to the machine. sEMG (surface electromyogram) can be observed when there is any effort in muscle even without actual movement, which is impossible with camera and accelerometer based NUI system. In sEMG based movement recognition system, the minimal number of electrodes is preferred to minimize the inconvenience. We analyzed the decrease in recognition accuracy as decreasing the number of electrodes. For the four kinds of movement intention without movement, extension (up), flexion (down), abduction (right), and adduction (left), the multilayer perceptron classifier was used with the features of RMS (Root Mean Square) from sEMG. The classification accuracy was 91.9% in four channels, 87.0% in three channels, and 78.9% in two channels. To increase the accuracy in two channels of sEMG, RMSs from previous time epoch (50-200 ms) were used in addition. With the RMSs from 150 ms, the accuracy was increased from 78.9% to 83.6%. The decrease in accuracy with minimal number of electrodes could be compensated partly by utilizing more features in previous RMSs.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.213-220
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• 2024

Fall detection and prevention technologies play a pivotal role in ensuring the well-being of individuals, particularly those living independently, where falls can result in severe consequences. This paper addresses the challenge of accurate and quick fall detection by proposing a Bayesian probability-based measure applied to surface electromyography (sEMG) signals. The proposed algorithm based on a Bayesian filter that divides the sEMG signal into transient and steady states. The ratio of posterior probabilities, considering the inclusion or exclusion of the transient state, serves as a scale to gauge the dominance of the transient state in the current signal. Experimental results demonstrate that this approach enhances the accuracy and expedites the detection time compared to existing methods. The study suggests broader applications beyond fall detection, anticipating future research in diverse human-robot interface benefiting from the proposed methodology.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1588-1593
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• 2008

This paper describes a real-time isometric pinch force prediction algorithm from surface electromyogram (sEMG) using multilayer perceptron (MLP) for human robot interactive applications. The activities of seven muscles which are observable from surface electrodes and also related to the movements of the thumb and index finger joints were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. Four muscles were selected for the force prediction by using the Fisher linear discriminant analysis among seven muscles, and the four muscle activities provided effective information for mapping sEMG to the pinch force. The MLP structure was designed to make training efficient and to avoid both under- and over-fitting problems. The pinch force prediction algorithm was tested on five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.112 ${\pm}$ 0.082 and CORR = 0.932 ${\pm}$ 0.058. These results imply that the proposed algorithm is useful to measure the produced pinch force without force sensors in real-time. The possible applications include controlling bionic finger robot systems to overcome finger paralysis or amputation.

• Journal of Biomedical Engineering Research
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• v.29 no.3
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• pp.205-211
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• 2008

A method of detecting the specific patterns related to hand motions using the surface EMG(electromyogram) on an arm is proposed and tested. To do this, we obtain separately modeling parameters based on the LP, Prony estimator, and calculate the latency shift value between channels by cross-correlation function. Then, the coefficients and latency shift value are applied to the detection method to classify the EMG signals related to hand motions. Compared with the conventional methods, the present method are more useful to detect the motion intention of the user as an input device in the mobile and wearable computing environments. And, We expect that the results of this study are helpful in the development of rehabilitation devices for the handicapped.