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Paper Details

Paper Title
Fuzzy Logic Controller for leg Movement Classification using sEMG Signal
The Surface electromyography is the most favourable method to witness muscle activity. It involves no risk to the subject as it is a non-invasive method. sEMG signals are processed and employed for rehabilitation engineering and varied prosthetic technologies. In these days, sEMG signals are used for development of numerous controlling prototypes based on gesture recognition modules. These modules distinguish different movements and utilise them to control a machine. This work proposed a classification of knee extension at three levels using fuzzy logic technique. Surface electromyography signals (sEMG) were acquired using hardware consisting of differential amplifier, non-inverting amplifier, band pass filter and interface module from Vastus lateralis muscle which is responsible for leg extension movements. MATLAB soft-scope was employed to import signals from hardware to system. For the task of classification, fuzzy logic controller was used. For signal analysis three parameters, Root Mean Square, Median and Standard Deviation were selected as inputs to fuzzy logic controller. Results showed that out of all three parameters, standard deviation was proved to be the best parameter for discriminating movements.
Surface Electromyography; Vastus Lateralis; Leg extension; Fuzzy Logic Controller; FIS Editor.
Others Details
Paper Id : 29332
Author Name : Iqbal Ahmed
Co-Author Name(s) : Kuldeep SinghGurpreet Singh
Volume/Issue No : Volume 05 Issue 03
Page No : 220-226
DOI Number : DOI:10.21090/IJAERD.29332
Publication Date : 2018-03-11
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
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Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406