• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.114-120
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• 2018

In this paper we implemented an EMG sensor system for wearable devices to obtain and analyze of EMG signals. The performance of the implemented sensor system is evaluated by the correlation analysis of muscle fatigue and muscle activation to clinical EMG system and compared with power consumption of the measured power of our system and commercial systems. In experiments with biceps and triceps brachii of 5 objects, The correlation values of muscle fatigue and muscle activation between our system and the clinical EMG system is 1.1~1.4 and about 1.0, respectively. And also the power consumption of our system is 25~50% less than that of some commercial EMG sensor systems.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.161-164
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• 2008

A realization for EMG signal sensing and vertical control system of robotizing arm is presented in this paper. The system is realized that a fine EMG bio-signals of humans' arm muscle are detected by surface electrode sensor, making a high performance amplifier and filtering, converting analog into digital signal and driving a servomotor for robotizing arm. The system is experimented by monitoring multiple step vertical control angles of robotizing arm corresponding to EMG signals in moving arm muscles. The experimental result are that the vertical control level is measured to around 2 degrees and mean error is 5% approximately.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.243-244
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• 2009

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.139-148
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• 2020

Walking is one of the most natural and repetitive actions we do in our daily lives. However, many modern people have problems with shoulders, back and spine due to incorrect walking habits. Therefore, it is becoming important to diagnose and correct wrong walking habits, for example, in-toeing, out-toeing, etc. early, which can be a precursor to various diseases. In this study, we developed the system to diagnose and prevent incorrect gait by grasping and analyzing the angle and muscle activity of the foot according to the typical wrong gait type through MPU 6050 acceleration sensor and the surface EMG sensor. Through a smartphone, numerical and visualization screens based on walking can be used to represent the angle of the feet, real-time EMG values, and even the number of steps. The correction effect was enhanced by improving the cognitive ability through a system that allows individuals to easily diagnose gait through smart devices and improve them according to their own problems.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.163-168
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• 2009

In this study, we would be developed the fuzzy controlled PGO that controlled the flexion and the extension of each PGO's hip joint using the bio-signal and FSR sensor. The PGO driving system is to couple the right and left sides of the orthosis by specially designed hip joints and pelvic section. This driving system consists of the orthosis, sensor, control system. An air supply system of muscle is composed of an air compressor, 2-way solenoid valve (MAC, USA), accumulator, pressure sensor. Role of this system provide air muscle with the compressed air at hip joint constantly. According to output signal of EMG sensor and foot sensor, air muscles and assists the flexion of hip joint during PGO gait. As a results, the maximum hip flexion angles of RGO's gait and PGO's gait were about $16^{\circ}\;and\;57^{\circ}$ respectively. The maximum angle of flexion/extention in hip joint of the patients during RGO's gait are smaller than normal gait, because of the step length of them shoes a little bit. But maximum angle of flexion/extention in hip joint of the patients during PGO's gait are larger than normal gait.

• 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.