Implementation of adaptive neuro fuzzy inference system for study of EMG-force relationship

The main objective of this study was to characterize the relationship between electromyography and force based on the results obtained from a developed analysis using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The developed method (ANFIS) presents interesting features for the study of thi...

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Bibliographic Details
Published in:	2017 International Conference on Electrical and Information Technologies (ICEIT) pp. 1 - 6
Main Authors:	Yassine, Elhamraoui, Abdelaziz, Belaguid, Larbi, Bellarbi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2017
Subjects:	Adaptive systems ANFIS Electrodes Electromyography EMG-Force Relationship Force Fuzzy logic Grip hand Muscles Protocols Surface electromyogram (sEMG)
Online Access:	Get full text
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Summary:	The main objective of this study was to characterize the relationship between electromyography and force based on the results obtained from a developed analysis using Adaptive Neuro Fuzzy Inference System (ANFIS) method. The developed method (ANFIS) presents interesting features for the study of this relationship. Among them, it can be highlighted the possibility of simultaneous analysis of various features, and the generation of graphics that allow the visualization of the relation between the EMG signals and the force. The method also allows the evaluation based on different models (linear, quadratic and exponential) allowing a better understanding of the EMG-force relationship. In order to evaluate the developed method (ANFIS) and study the EMG-force correlation. Electromyographic signals (EMG) were detected on the frontarm from 9 subjects while executing 3 levels of force subjective caused by the grip hand. The results showed that statistical features related to the amplitude of the signal are more appropriate to represent the relationship between EMG and force during the execution of force. These results, besides having several practical applications, can be used as part of EMG signals simulators, developed for different applications, such as the evaluation of automatic systems used in the decomposition of EMG signals.
DOI:	10.1109/EITech.2017.8255292