| Title |
A Study on the Low Force Estimation of Skeletal Muscle by using ICA and Neuro-transmission Model |
| Authors |
유세근(Yoo, Sae-Keun) ; 염두호(Youm, Doo-Ho) ; 이호용(Lee, Ho-Yong) ; 김성환(Kim, Sung-Hwan) |
| Keywords |
EMG ; Low Force ; ICA ; Neuro-Transmission Model ; IPI |
| Abstract |
The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns. |