Pattern Recognition Of EMG Signal During Load Lifting Using Artificial Neural Network (ANN)

Noor Faiza, Kamaruzaman (2015) Pattern Recognition Of EMG Signal During Load Lifting Using Artificial Neural Network (ANN). Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) Pattern Recognition Of EMG Signal During Load Lifting Using Artificial Neural Network (ANN) 24 Pages.pdf Download (320kB)

Abstract

This research describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a technique to quantify and record the muscle action when people perform certain operation and activities. This research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on EMG signal is not an easy task because of the nonlinearities behavior of the signal. It required a good classifier to distinguish each pattern. The motivation of this project is to help the person suffer with hemiparesis to perform daily activities as well as to improve the lifestyle. It is important for patients to realize the hopes of hemiparesis after experiencing their inability to do activity as a normal human. Recognizing EMG pattern is crucially important for design the prosthesis arm that enables the patients to lift the heavy load despite of their muscle weaknesses. Therefore, a proper analysis of muscle behavior is necessary. The objectives of this research are to extract the important features of EMG signal using time domain analysis and to classify EMG signal based on load lifting using ANN. The analysis was performed to five subjects that were chosen for the most part in view of criteria determined. The EMG signal are gained at long head biceps brachii. At that point, the subjects were solicited to lift the heaps from 2kg, 5kg, and 7kg.It is expected an accurate classifier which can recognize the pattern precisely and could be further used for design the prosthesis arm.

Actions (login required)

View Item