• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.769-771
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• 2000

In this paper, pattern recognition scheme for EMG signal using artificial neural network is proposed. For manipulating ability, the movements of human arm are classified into several categories EMG signals of appropriate muscles are collected during arm movement. Patterns of EMG signals of each movement are recognized as follows: 1) The features of each EMG signal are extracted. 2) With these features, the neural network is trained by using feedforward error back-propagation (FFEBP) algorithm. The results show that the arm movements can be classified with EMG signals at high accuracy.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

• 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.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.9-15
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• 2008

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.243-249
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• 2005

The number of people who have lost limbs due to amputation has increased due to various accidents and diseases. Numerous attempts have been made to provide these people with prosthetic devices. These devices are often controlled using myoelectric signals. Although the success of fitting myoelectric signals (EMG) for single device control is apparent, extension of this control to more than one device has been difficult. The lack of success can be attributed to inadequate multifunctional control strategies. Therefore, the objective of this study was to develop multifunctional myoelectric control strategies that can generate a number of output control signals. We demonstrated the feasibility of a neural network classification control method that could generate 12 functions using three EMG channels. The results of evaluating this control strategy suggested that the neural network pattern classification method could be a potential control method to support reliability and convenience in operation. In order to make this artificial neural network control technique a successful control scheme for each amputee who may have different conditions, more investigation of a careful selection of the number of EMG channels, pre-determined contractile motions, and feature values that are estimated from the EMG signals is needed.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.142-148
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• 2016

Recently, human identification using various biological signals has been studied and human identification based on the gait has been actively studied. In this paper, we propose a human identification based on the EMG(Electromyography) signal of the thigh muscles that are used when walking. Various features such as RMS, MAV, VAR, WAMP, ZC, SSC, IEMG, MMAV1, MMAV2, MAVSLP, SSI, WL are extracted from EMG signal data and ANN(Artificial Neural Network) classifier is used for human identification. When we evaluated the recognition ratio per channel and features to select approptiate channels and features for human identification. The experimental results show that the rectus femoris, semitendinous, vastus lateralis are appropriate muscles for human identification and MAV, ZC, IEMG, MMAV1, MAVSLP are adaptable features for human identification. Experimental results also show that the average recognition ratio of method of using all channels and features is 99.7% and that of using selected 3 channels and 5 features is 96%. Therefore, we confirm that the EMG signal can be applied to gait based human identification and EMG signal based human identification using small number of adaptive muscles and features shows good performance.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• Journal of Information Technology Services
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• v.7 no.2
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• pp.149-164
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• 2008

As the computing landscape is shifting to ubiquitous computing environments, there is increasingly growing the demand for a variety of device controls that react to user's implicit activities without excessively drawing user attentions. We developed an EMG-based car interface that enables the physically handicapped to drive a car using their functioning peripheral nerves. Our method extracts electromyogram signals caused by wrist movements from four places in the user's forearm and then infers the user's intent from the signals using multi-layered neural nets. By doing so, it makes it possible for the user to control the operation of car equipments and thus to drive the car. It also allows the user to enter inputs into the embedded computer through a user interface like an instrument LCD panel. We validated the effectiveness of our method through experimental use in a car built with the EMG-based interface.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.402-405
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• 2000

In this paper we propose a method ef pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through leaning and therefore it can classify input patterns. The raw EMC signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.