• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.286-292
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• 2009

This paper proposes a knee-wearable robot system for assisting the muscle power of human knee by processing EMG (Electromyogram) signals. Although there are many muscles affecting the knee joint motion, the rectus femoris and biceps femoris among them play a core role in the extension and flexion motion, respectively, of the knee joint. The proposed knee-wearable robot system consists of three parts; the sensor for measuring and processing EMG signals, controller for estimating and applying the required knee torque, and actuator for driving the knee-wearable mechanism. Ultimately, we suggest the motion control method for knee-wearable robot system by processing the EMG signals of corresponding two muscles in this paper. Also, we show the effectiveness of the proposed knee-wearable robot system through the experimental results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.10
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• pp.934-939
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• 2011

This paper experimentally deals with the relationship between the ankle electromyogram(EMG) and walking motion in order to activate the ankle joint of a walking-assistance robot for rehabilitation. Based on the anatomical structure and motion pattern of an ankle joint, major muscles were selected for EMG measurements. Surface EMG signals were monitored for several human bodies at various stride distances and stride frequencies. Root-mean-squared magnitude of EMG signals were related with the walking conditions. It appeared that the magnitude of the ankle EMG signal was linearly proportional to the stride distance and stride frequency, and thus to the walking speed.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous 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 contrasts the multilayer feedforward networks are suitable for static patterns. Ank 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 MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.378-381
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• 2002

In this paper, we proposed an electromyography(EMG) based control method of a virtual robot arm as an adaptive human supporting system or remote control system, which consists of an shoulder control part, elbow control part, and wrist control part. The system uses four surface electrodes to acquire the EMG signal from operator. It is shown from the experiments that the EMG patterns during arm motions can be classified sufficiently by using SOM and LVQ. The interface system based on PC environment is constructed to 3-D graphic user interface(GUI) program. Experimental results show that proposed method obtains approximately 94 percent of success in classification.

• 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 Korea Multimedia Society
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• v.19 no.1
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• pp.68-79
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• 2016

Electromyographic (EMG) signals have been widely used as motion commands of prosthetic arms. Although EMG signals contain meaningful information including the movement intentions of human body, it is difficult to predict the subject's motion by analyzing EMG signals in real-time due to the difficulties in extracting motion information from the signals including a lot of noises inherently. In this paper, four Ag/AgCl electrodes are placed on the surface of the subject's major muscles which are in charge of four upper arm movements (wrist flexion, wrist extension, ulnar deviation, finger flexion) to measure EMG signals corresponding to the movements. The measured signals are sampled using DAQ module and clustered sequentially. The Fuzzy C-Means (FCMs) method calculates the center values of the clustered data group. The fuzzy system designed to detect the upper arm movement intention utilizing the center values as input signals shows about 90% success in classifying the movement intentions.

• Journal of Life Science
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• v.20 no.11
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• pp.1704-1710
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• 2010

The purpose of this study was to describe the biomechanical characteristics of stroke patients. These characteristics were obtained during walking on a Zebris system, cinematography system and EMG system. Seven female stroke patients participated in this study. The magnitude of the profiles (joint peak angle, joint peak moments, foot pressure COP, EMG data) correlated with rehabilitation training duration using t-test. The significance level selected for this study was p<0.05, t-test. Joint analysis identified significant differences in hip joint peak angle and hip joint peak moment. Foot pressure verified significant differences in gait line length of COP. The EMG signal proved significant differences in rectus femoris and vastus lateralis.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.4
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• pp.108-113
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• 2011

In this paper, we propose a new method for robot navigation control through EMG and acceleration sensors which is attached to wrist. The method can remote control with intuitive motion like driving a car. It decide to control whether or not through EMG signal processing. And motion inferring through signal processing from acceleration sensor. Inferred motion is mapped to control command such as 'Forward', 'Backward', 'Left', 'Right'. Accuracy of each motions are over 99%. Control is capable naturally without time delay. Entire system has been implemented and we verified its utility through demonstration.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.152-157
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• 2012

In this paper, we develop a muscle exercise prescription system and present a prescription method by analyzing bioelectric signal of human muscles. This system is designed to give right exercise prescriptions include strength, duration, frequency of exercise after diagnosing personal body condition using EMG(Electromyography). With the help of these prescriptions all users can keep there optimum exercise status and avoid excess exercise symptom and, we van utilize in all the measurements like abnormal posture, muscle power, muscle regidity, muscle fatigue, muscle balance. Also easily accessable system can offer variable utilizations such as in health care center, sports center, social welfare center, social medical center, school, and kinder garden.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.