• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.1
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• pp.73-80
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• 2015

In this paper, we propose the robotic arm control method based on upper extremity electromyogram for lower upper extremity amputation patient. The muscle activity of the forearm flexor, forearm extensor and biceps was analyzed to utilize distribution of muscle activity to a specific position in order to the control input. This control input is converted into a control command for controlling the robotic arm through the algorithm. For the experiment and verify the proposed method, 5DoF robotic arm control system was constructed with 1 channel EMG Module and PC applications through the interworking with each module to perform a three-channel EMG analysis. For accuracy and performance evaluation of control, Experiments were performed with robotic arms moving objects. As a result of experiments which after training for 10 hours by middle 20's man, Validity of the proposed method was evaluated based an average accuracy of 92.5%.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.89-95
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• 2015

Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.382-392
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• 2023

Objective : The use of indocyanine green videoangiography (ICG-VA) to assess blood flow in the brain during cerebrovascular surgery has been increasing. Clinical studies on ICG-VA have predominantly focused on qualitative analysis. However, quantitative analysis numerical modelling for time profiling enables a more accurate evaluation of blood flow kinetics. In this study, we established a multiple exponential modified Gaussian (multi-EMG) model for quantitative ICG-VA to understand accurately the status of cerebral hemodynamics. Methods : We obtained clinical data of cerebral blood flow acquired the quantitative analysis ICG-VA during cerebrovascular surgery. Varied asymmetric peak functions were compared to find the most matching function form with clinical data by using a nonlinear regression algorithm. To verify the result of the nonlinear regression, the mode function was applied to various types of data. Results : The proposed multi-EMG model is well fitted to the clinical data. Because the primary parameters-growth and decay rates, and peak center and heights-of the model are characteristics of model function, they provide accurate reference values for assessing cerebral hemodynamics in various conditions. In addition, the primary parameters can be estimated on the curves with partially missed data. The accuracy of the model estimation was verified by a repeated curve fitting method using manipulation of missing data. Conclusion : The multi-EMG model can possibly serve as a universal model for cerebral hemodynamics in a comparison with other asymmetric peak functions. According to the results, the model can be helpful for clinical research assessment of cerebrovascular hemodynamics in a clinical setting.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.12
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• pp.808-813
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• 2008

The study was to evaluate the air-conditioning of sleep algorithm. The algorithm was developed through the analysis of brain waves and MST, the experiments using air conditioner was performed in a apartment bedroom. Five female subjects were participated for the experiment. Eight hours of data collection a day was performed under different algorithm, case A, case B and case C. Physiological signals, EEG, ECG, EOG, and EMG, were obtained using polygraph and converted into digital signal. Then, subjects were asked to answer the questionnaire about their thermal sensation after experiment in bedroom. Sleep stages were classified, then TST, Sleep latency and Sleep efficiency were calculated for the three different air conditioner algorithm. As results, TST, Sleep efficiency, questionnaire showed the higher values for Case B algorism than that for other algorism. On the other hand, SWS latency was lower than for other conditions. Therefore, it was concluded that Case B of the algorithm was the best for comfortable and deep sleep.

• Journal of the Ergonomics Society of Korea
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• v.29 no.3
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• pp.357-364
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• 2010

This study proposes a lower-limb exoskeleton system that is controlled by a wearer's muscle activity. This system is designed by following procedure. First, analyze the muscle activation patterns of human leg while walking. Second, select the adequate actuator to support the human walking based on calculation of required force of knee joint for step walking. Third, unit type knee and ankle orthotics are integrated with selected actuator. Finally, using this knee-assistive system (KAS) and developed muscle stiffness sensors (MSS), the muscle activity pattern of the subject is analyzed while he is walking on the stair. This study proposes an operating algorithm of KAS based on command signal of MSS which is generated by motion intent of human. A healthy and normal subject walked while wearing the developed powered-knee exoskeleton on his/her knees, and measured effectively assisted plantar flexor strength of the subject's knees and those neighboring muscles. Finally, capabilities and feasibility of the KAS are evaluated by testing the adapted motor pattern and the EMG signal variance while walking with exoskeleton. These results shows that developed exoskeleton which controlled by muscle activity could help human's walking acceptably.

• 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 the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1635-1638
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• 1997

A measurement of conduction veloctiy of the action potentials along the muscle fibres has been applied to various diagnosis. When we measure muscle fiber conduction velocity, it occurs that not only change of conduction velocity but alos inclusion of mipulse component by physiological and experimental reason. So, robuster time delay estimation algorithm than general methods[1] is needed to find correct time delay form these signals. In this paper we, propose new time delay estimation algorithms, robust in impulsive noise, by using characteristic of .alpha.-stable distribution whcih defines impulsive noise well. Then we apply proposed algorthms to measure muscle fiber conduction velocity and compare them with other studies.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.887-888
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• 2006

In this paper, we devised mac-yule detection system which provide resting state mac-yule. The devised system composed of signal transformation part, signal processing part, and PC based display part. Hardware part consisit of PPG, ECG, EEG, EMG, and RSP. Also, software system consist of bio-signal processing software which detecting mac-yule. EEG-$\alpha$, $\beta$ wave analysis algorithm that use wavelet transformation, RSP detecting algorithm which used zero-crossing method.