• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.62-69
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• 2020

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.3
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• pp.141-146
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• 2008

The purpose of this study is to develop the algorithm which can control muscle power assist robot especially for elderly. Recently, wearable robots for power assistance are developed by many researchers, and its application fields are also variable such as for medical or military equipment. However, there are many technical barriers to develop the wearable robot. This study suggest a control method improving performance of a wearable robot system by using a EMG signal of major muscles and a force sensor signal as command signal of system. The result of the robot Prototype efficiency experiment, the case of Maximum Isometric motion it suggest 100% power of muscle, the man need only 66% of MVIC(Maximum Voluntary Isometric Contraction) to lift 5kg dumbbell without robot assist. However the man needs only 52% of MVIC to lift 5kg dumbbell with robot assist. Therefore 20% muscle power increased with robot assist. Also, we designed light weight robot mechanism that extract the command signal verified and drive the wanted motions.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.365-370
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• 2016

The study about VR (Virtual Reality) has been done from the 1960s, but technical limits and high cost made VR hard to commercialize. However, in recent, high resolution display, computing power and 3D sensing have developed and hardware has become affordable. Therefore, normal users can get high quality of immersion and interaction. However, HMD devices which offer VR environment have high latency, so it disrupts the VR environment. People are usually sensitive to relative latency over 20ms. In this paper, as adding the Electromyogram (EMG) sensors to typical IMU sensor only system, the latency reduction method is proposed. By changing software and hardware components, some cases the latency was reduced significantly. Hence, this study covers the possibility and the experimental verification about EMG sensors for reducing the latency.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.160-163
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• 1995

An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Journal of the Korean Society of Physical Medicine
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• v.3 no.3
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• pp.151-160
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• 2008

Purpose : The purpose of this study is to know the muscle activation after sub-talar joint mobilition and active exercise, and the low leg muscle activity through the well-balanced interaction of ankle joint around muscle. Methods : For this study 61 experimental subjects are divided into 24 people of supination foot group, pronation foot group 17 people of, 20 people of control group through navicular drop test. Surface EMG was used in order to measure the muscle activities. following is the result of the data analysis about each experiment that has been carried on a week, 2 weeks, 3 weeks, 4 weeks before, and even comparing with pre-experimental state. Results : In electromyogram study, the higher muscle activation there was before the experiment, the more muscle activation increase there was after the experiment in Tibialis Anterior, Peroneus Longus, Peroneus Brevis.(p<.05). Conclusion : This study shows the balanced activation of foot and ankle-around muscle. It shows that foot shape affects the balanced activation recovery of lower leg muscles.

• Journal of International Academy of Physical Therapy Research
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• v.4 no.1
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• pp.494-498
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• 2013

The purpose of this study is to find out the activity of gluteus medius muscle by the changes of load given to the upper limbs in single support phase. This study was twenty healthy men from A College. The left gluteus medius muscle was measured using SEMG(surface Electromyogram). Only the left foot was supported, and for the right foot, the right upper limbs were abducted while hip joint and the knee joint were bent at a $90^{\circ}$ angle. The study was made by giving weight using dumbbells, depending on the RM of the subject(0RM, 1RM, 3RM, 5RM, 7RM). Gluteus medius muscle showed a large activity for those given weight to the upper limbs(1RM, 3RM, 5RM, 7RM) than that without weight(0RM). There was a significant difference in the activity of gluteus medius muscle between each RM. Gluteus medius muscle is high active when weight is given to the upper limbs in single support phase.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.4
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• pp.9-17
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• 2018

PURPOSE: This study was conducted to compare the effects of the Graston-based massage method and microcurrent application on muscle fatigue after causing muscle fatigue of the quadriceps femoris by applying high-intensity exercise. METHODS: Study subjects (56 healthy subjects in their 20s) were randomized into a microcurrent group (MG), Graston group (GG), and control group (CG). To induce fatigue of the quadriceps femoris, the subjects performed squats 100 times without a break within about 5 minutes. Muscle fatigue was measured immediately after performing squats and 15 minutes after intervention. To measure muscle fatigue, surface electromyogram (EMG) was used to obtain and compare the median frequency. Microcurrent or Graston massage was applied to their quadriceps muscles 15 minutes after performing the squats. RESULTS: Muscle fatigue increased significantly in the vastus lateralis before and after intervention in the Graston group (p<.05), while no significant difference was observed in the rectus femoris and vastus medialis (p>.05). There were also no significant differences in the microcurrent group or the control group. CONCLUSION: Both the microcurrent group and Graston group showed a tendency for increasing median frequency values compared to the control group, but there was no significant difference except in the vastus lateralis treated with the Graston technique. Although there was no significant difference, the Graston technique could be utilized to reduce the occurrence of DOMS by preventing muscle fatigue in clinical practice or sports medicine.

• 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%).

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.209-215
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• 2019

In order to provide a safe free-weight-training environment to people without workout trainers, we suggest a smart bench press machine with an automatic weight control system sensitive to user tiredness. Physical weight plates on the machine are replaced with a hydraulic cylinder as a press load and the cylinder knob is coupled with a step motor to change its tensile force automatically in-between lifting exercises. Three subjects participated to verify the usability of the smart bench press machine. They were asked to lift a 6-RM press load 10 times with 3 different lifting conditions: 1) no assistance, 2) a human assistance, and 3) the automatic weight control. All subjects were not able to complete the 10 sets without assistance due to tiredness, but they finished the full sets under the two assistive conditions. Average lifting speeds under the automatic weight control condition showed the most consistent level. Normalized quasi-tension data based on surface electromyogram signals of both Pectoralis Majors revealed that the subjects maintained the target muscle activation level above 50% but not more than 80% throughout the 10 sets. Therefore, the smart bench press machine is expected to both keep pace with the lifting exercise and reduce risk of injuries due to excessive muscle tensions.