Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions
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,...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
International Workshop on Computer Science and Engineering (WCSE)
2016
|
| Subjects: |
| _version_ | 1796862111442272256 |
|---|---|
| author | Nazmi, N. Shin-Ichiroh, Y. Rahman, M. A. A. Ahmad, S. A. Adiputra, D. Zamzuri, H. Mazlan, S. A. |
| author_facet | Nazmi, N. Shin-Ichiroh, Y. Rahman, M. A. A. Ahmad, S. A. Adiputra, D. Zamzuri, H. Mazlan, S. A. |
| author_sort | Nazmi, N. |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T20:06:37Z |
| format | Conference or Workshop Item |
| id | utm.eprints-73656 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:06:37Z |
| publishDate | 2016 |
| publisher | International Workshop on Computer Science and Engineering (WCSE) |
| record_format | dspace |
| spelling | utm.eprints-736562017-11-28T07:42:36Z http://eprints.utm.my/73656/ Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions Nazmi, N. Shin-Ichiroh, Y. Rahman, M. A. A. Ahmad, S. A. Adiputra, D. Zamzuri, H. Mazlan, S. A. T Technology (General) 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. International Workshop on Computer Science and Engineering (WCSE) 2016 Conference or Workshop Item PeerReviewed Nazmi, N. and Shin-Ichiroh, Y. and Rahman, M. A. A. and Ahmad, S. A. and Adiputra, D. and Zamzuri, H. and Mazlan, S. A. (2016) Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions. In: 2016 6th International Workshop on Computer Science and Engineering, WCSE 2016, 17-19 June 2016, Tokyo, Japan. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982822107&partnerID=40&md5=a0c079e4053da5cb143bc13ecad6e11f |
| spellingShingle | T Technology (General) Nazmi, N. Shin-Ichiroh, Y. Rahman, M. A. A. Ahmad, S. A. Adiputra, D. Zamzuri, H. Mazlan, S. A. Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title | Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title_full | Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title_fullStr | Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title_full_unstemmed | Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title_short | Fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| title_sort | fuzzy logic for walking patterns based on surface electromyography signals with different membership functions |
| topic | T Technology (General) |
| work_keys_str_mv | AT nazmin fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT shinichirohy fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT rahmanmaa fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT ahmadsa fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT adiputrad fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT zamzurih fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions AT mazlansa fuzzylogicforwalkingpatternsbasedonsurfaceelectromyographysignalswithdifferentmembershipfunctions |