• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.106-111
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• 1989

A new EMG signal processing algorithm for SMUAP pattern classification is proposed. It checks the combination and regularity of ISI using a spike counter as a decision making routine, and performs SMUAP waveform alignment in frequency domain and selects spikes through FIR filtering. As a result, with the EMG signals recorded during 5 seconds at 10-50% MVC force level, the SMUAP ranged from five to nine units were classified and identification rate is greater than 55 percent using a concentric needle electrode. In the IBM PC/AT the processing time typically required 2 minutes.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.81-92
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• 2011

In this paper, a new method to estimate the number of MU (motor unit) related to the widths and distribution of end plate in NMJ (neuromuscular junction) of biceps brachii is proposed by varying muscle parameter statistically in EMG model. This work is done by designing MU-simulator and EPZ-simulator. The proposed method was compared with the results of previous researchers. The proposed MU-simulator generates SMUAP (single motor unit action potential) and CMAP (compound muscle action potential) signal similar to detected SMUAP and CMAP signal obtained from muscle. The EPZ-simulator estimate the numbers of MU by varying the widths and distribution of end plate in neuromuscular junction of muscle. The results shows that the numbers of MU was estimated about 450 ea. and muscle fibers was about 340 ea., end plate widths was about 6 mm, and end plate was randomly distributed. The proposed method may be comparable with the method of anatomical studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.2040-2050
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• 2007

In this study, we present a novel method for estimating the information of MU(motor unit) which is the basic element of human muscle by using surface EMG. Some of the method developed in this field could only estimate the numbers of MU that is activated. However, in our study the MU-simulator based on the line source model was designed to estimate the MU information including the numbers of MU and muscle fiber, conduction velocity, MU diameter, fiber diameter, and end plate position. The SMUAP(single motor unit action potential) detector was designed and CMAP(compound muscle action potential) by electrical stimulus was recorded. With these data, the MU-simulator can estimate the MU information by varying muscle paramater settings through MSE(mean square error) method. Our results shows that the proposed method can be comparable with the method of anatomical studies. Moreover, our system can be utilized to build a tool for diagnosis and treatment assessment of neuromuscular patients.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.28-39
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• 2010

In this paper, a new method to estimate MU (motor unit) location in the short head of BIC (biceps brachii) muscle using surface EMG (electromyogram) is proposed. The SMUAP (single motor unit action potential) is generated from a MU located at certain depth from the skin surface. The depth is referred as MU location. For estimating muscle force precisely, the information of the MU location is required. The reference SMUAPs are simulated based on anatomical structure of human muscle, and compared with acquired real EMG signals using 3-channel surface EMG electrode. The proposed method was compared with the results of previous researchers and verified its accuracy by computer simulation. From the simulation result in case of the MU located in 8[mm], the average estimation error of proposed method was 0.01[mm]. But the average estimation error of Roeleveld's method was 2.33[mm] and Akazawa's method was 1.70[mm]. Therefore the proposed method was more accurate than the methods of previous researchers.