• Journal of Biomedical Engineering Research
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• v.10 no.1
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• pp.11-16
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• 1989

This paper describes the AR model of EMG signal during maximum voluntary contraction. By comparing the AR coefficients and the reflection coefficients of the AR model with the median frequency of power spectrum, it is proved that muscle fatigue can be measured by the AR and the reflection coefficients. In the estimation procedure of AR model parameter, the autocorrelation method is superior to the covariance method, and it is determined that the optimal order is six. As the muscle becomes fatigue, the median frequency of power spectrum is declined, and the AR coefficient [$a_1$] and the reflection coefficient [$k_1$] are also decreased. Therefore the muscle fatigue can be measured by the AR parameter.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.330-335
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• 1999

The goal of the study is to determine the most sensitive parameter to represent the degree of muscle force and fatigue. Various numerical parameters such as the first coefficient of Autoregressive (AR) Model, Root Mean Square (RMS), Zero Crossing Rate (ZCR), Mean Power Frequency (MPF), Median Frequency (MF) were tested in this study. Ten healthy male subjects participated in the experiment. They were asked to extend their trunk by using the right and left erector spinae muscles during a sustained isometric contraction for twenty seconds. The force levels were 15%, 30%, 45%, 60%, and 75% of Maximal Voluntary Contraction (MVC), and the order of trials was randomized. The results showed that RMS was the best parameter to measure the force level of the muscle, and that the first coefficient of AR model was relatively sensitive parameter for the fatigue measurement at less than 60% MVC condition. At the 75% MVC, however, both MPF and the first coefficient of AR Model showed the best performance in quantification of muscle fatigue. Therefore, the sensitivity of measurement can be improved by properly selecting the parameter based upon the level of force during a sustained isometric condition.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.155-161
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• 2008

Muscle force prediction in forward dynamic analysis of human motion depends many muscle parameters associated with muscle actuation. This research studies the effects of various parameters of Hill type muscle model using the simple hand raising motion. Motion analysis is carried out using motion capture system, and each muscle force is recorded for comparison with muscle model generated muscle force. Using Hill type muscle model, muscle force for generating the same hand rasing motion was setup adjusting 5 activation parameters. The test showed the importance of activation parameters on the accurate generation of muscle force.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.16-23
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.415-416
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• 2011

• Journal of Biomedical Engineering Research
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• v.30 no.3
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• pp.241-246
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• 2009

Since inappropriate muscle forces mean that people cannot perform some activities related to roles of the muscle, muscle forces have been considered as an important parameter in clinic. Therefore, many methods have been introduced to estimate muscle forces indirectly. One of the methods is muscle tissue dynamics and it is widely used in commercial softwares including musculoskeletal model, such as SIMM. They, however, need motion data captured from 3-dimensional motion analysis system. In this study, we introduced an algorithm to estimate muscle forces in real-time by using joint angles. The heel-rise movements were performed for a normal with 3-dimensional motion analysis system, EMG measurement system, and electrogoniometers. Joint angles obtained from electrogoniometers and EMG signals were used to estimate muscle forces. Simulation was performed to find muscle forces using motion data which was imported into musculoskeletal software. As the results, muscle lengths and forces from the developed algorithm were similar to those from commercial software in pattern. Results of this study would be helpful to implement a tool to calculate reasonable muscle forces in real-time.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.76-81
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1388-1400
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• 2004

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.380-388
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• 2004

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.

• The Journal of Korean Physical Therapy
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• v.27 no.6
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• pp.386-391
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• 2015

Purpose: This study investigated whether the strength imbalance between two muscles can affect the score of EMG based biofeedback game, and whether the EMG based biofeedback game score can be used as predictable indicator of the degree of muscle balance alternating the conventional strength measuring equipment. Methods: 40 participated in this study. Biodex was used to measure the peak torque/weight in order to calculate the muscle strength balance index between plantar flexor and dorsiflexor of ankle joint. And muscle balance index (MBI) was calculated. The EMG biofeedback game scores of dorsiflexor and plantar flexor were acquired, so that the EMG electrodes were attached at tibialis anterior and gastrocnemius. The relationship between the game score and the muscle balance index were analyzed. Results: There was negative correlation between the muscle balance index between plantar flexor and dorsiflexor and the peak torque/weight of plantar flexor (r=-0.70). And there was negative correlation between the muscle balance index between plantar flexor and dorsiflexor and the game score of plantar flexor (r=-0.83). Conclusion: The EMG biofeedback game score had significant relationship with muscle imbalance at ankle joint, so it seems that the game score can be used for predicting the degree of muscle imbalance as a parameter.