EMG-Signal-for-Gesture-Recognition ✋

Machine learning based EMG Signal analysis for Gesture Recognition

Paper link	Dataset link

Content

Overview
Code explaination
Model evaluation
Taem members

1) Overview

-The paper we are discussing titled "Machine Learning-Based Hand Gesture Recognition via EMG Data"
 by Zehra Karapinar Senturk and Melahat Sevgul Bakay focuses on a machine learning-based 
 approach for hand gesture recognition using EMG data

-The authors explore the use of machine learning algorithms to recognize and classify specific hand gestures
 based on the patterns observed in the EMG signals.

-Our main application is virtual rehabilitation by combining gesture recognition with VR technology.

2) Code explaination

Requirements

sklearn == 1.2.2
numpy==1.22.4
pandas == 2.0
matplotlib == 3.6.0
scipy == 1.10.1

A)Preprocessing:

preprocess the EMG raw signal data by applaying Notch filter then Band pass filter.

`Raw EMG signal`	`Notch filtered signal`	`Band pass filtered signal`

B) Feature extraction & selection:

After we preprocess the EMG signal , we now do feature extraction to extract the following 12 features from each of the 8 channels:

`integrated EMG IEMG`	`simple square integrated SSI`	`Root mean squared rms`	`mean absolute value MAV`	`Variance`	`waveform length WL`	`peak to peak ptp`	`difference absolute mean value DAMV`	`difference absolute standard deviation value DASDV`	`Willison amplitude WAMP`	`min`	`max`

`features`	`after extraction`

Then , we apply feature selection techniques Kbest in our case

`Feature selection`	`Feature scores`

C) Classification:

we used the following three classifiers to construct our model:

`classifier`	`code`
`Random forest`
`SVM`
`MLP neural network`

3) Model evaluation

Performance comparsion

`Classifier`	`Confusion Matrix`
`SVM`
`RF`
`MLP`

ayaamrr/EMG-Signal-for-Gesture-Recognition