TY - GEN
T1 - Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions
AU - Nazmi, Nurhazimah
AU - Shin-Ichiroh, Yamamoto
AU - Rahman, Mohd Azizi Abdul
AU - Ahmad, Siti Anom
AU - Adiputra, Dimas
AU - Zamzuri, Hairi
AU - Mazlan, Saiful Amri
PY - 2016
Y1 - 2016
N2 - Classifying walking patterns is important in developing assistive robotic devices, especially for lower limb rehabilitation. Recently, Fuzzy Logic (FL) controllers have successfully been applied in grasping and control system for upper limb based on surface Electromyography (EMG) signals. Therefore, this paper evaluates the performance of FL with different membership functions in discriminating walking phases (e.g, stance and swing phases). The accuracy of two widely used membership functions (MF) like triangular and Gaussian is compared to identify their behavior for detecting the phases of walking. In this study, the MATLAB and Simulink toolboxes are used to examine the performance of each MF. Our findings show Gaussian MF gained better performance than the triangular MF with 90% of classification accuracy. Therefore, the Gaussian MF could be the best solution to classify the walking phases in this work.
AB - Classifying walking patterns is important in developing assistive robotic devices, especially for lower limb rehabilitation. Recently, Fuzzy Logic (FL) controllers have successfully been applied in grasping and control system for upper limb based on surface Electromyography (EMG) signals. Therefore, this paper evaluates the performance of FL with different membership functions in discriminating walking phases (e.g, stance and swing phases). The accuracy of two widely used membership functions (MF) like triangular and Gaussian is compared to identify their behavior for detecting the phases of walking. In this study, the MATLAB and Simulink toolboxes are used to examine the performance of each MF. Our findings show Gaussian MF gained better performance than the triangular MF with 90% of classification accuracy. Therefore, the Gaussian MF could be the best solution to classify the walking phases in this work.
KW - Classification
KW - Fuzzy logic
KW - Pattern recognition
KW - Walking phases
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M3 - Conference contribution
AN - SCOPUS:84982822107
T3 - 2016 6th International Workshop on Computer Science and Engineering, WCSE 2016
SP - 636
EP - 639
BT - 2016 6th International Workshop on Computer Science and Engineering, WCSE 2016
PB - International Workshop on Computer Science and Engineering (WCSE)
T2 - 2016 6th International Workshop on Computer Science and Engineering, WCSE 2016
Y2 - 17 June 2016 through 19 June 2016
ER -