• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.205-211
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• 2007

In this paper, the prototype of surface EMG (ElectroMyoGram) sensor is developed for the robotic rehabilitation applications, and the developed sensor is composed of the electrodes, analog signal amplifiers, analog filters, ADC (analog to digital converter), and DSP (digital signal processor) for coding the application example. Since the raw EMG signal is very low voltage, it is amplified by about one thousand times. The artifacts of amplified EMG signal are removed by using the band-pass filter. Also, the processed analog EMG signal is converted into the digital form by using ADC embedded in DSP. The developed sensor shows approximately the linear characteristics between the amplitude values of the sensor signals measured from the biceps brachii of human upper arm and the joint angles of human elbow. Finally, to show the performance of the developed EMG sensor, we suggest the application example about the real-time human elbow motion acquisition by using the developed sensor.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.815-816
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• 2006

In this paper, an EMG (Electromyography) sensor for utilizing an EMGl-based HCI are described. The EMG sensor is a dry type and has high gain (1000-10000). Therefore, this sensor can be properly applied to HCI devices using EMG signals without additional amplification circuit.

• 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.

• 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%).

• 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 Biomedical Engineering Research
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• v.40 no.6
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• pp.223-229
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• 2019

The number of knee-related disease patients and knee joint surgeries is steadily increasing every year, and for knee rehabilitation training for these knee joint patients, it is necessary to strengthen the muscle of vastus medialis and quadriceps femoris. However, because of the cost and time-consuming difficulties of receiving regular hospital treatment in the course of knee rehabilitation, we developed knee exoskeleton using rapid prototype for knee rehabilitation with feedback from the electromyogram (EMG) and inertia motion unit (IMU) sensor. The modules was built on the basis of EMG and an IMU sensor applied complementary filter, measuring muscle activity in the vastus medialis and the range of joint operation of the knee, and then performing the game based on this measurement. The IMU sensor performed up to 97.2% accuracy in experiments with ten subjects. The functional game contents consisted of an exergaming platform based on EMG and IMU for the real-time monitoring and performance assessment of personalized isometric and isotonic exercises. This study combined EMG and IMU-based functional game with knee rehabilitation training to enable voluntary rehabilitation training by providing immediate feedback to patients through biometric information, thereby enhancing muscle strength efficiency of rehabilitation.

• 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.

• 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.