Implementation of Feature Extraction of Neuro Muscular EMG Signal
Objective
EMG signals are essential in various fields, including healthcare, robotics, and human-computer interaction. This project aims to contribute to the understanding and utilization of EMG data for a wide range of applications
Abstract
EMG, which is frequently used in a range of professions, can be used to study muscle activation. A technique for detecting electrical activity in the muscles is an EMG. The EMG signals, which offer a wealth of details on muscle activity, can be analyzed using a number of different techniques. Electromyography (EMG) is a technique used to ascertain whether electrical activity is present in muscle signals. Denoising is a common technique for restoring the original quality of the source data while dealing with noisy signals. It makes an effort to lower noise in the unprocessed EMG signals while maintaining pertinent information. Denoising is therefore essential in a wide range of disciplines, including medicine and the health sciences. After denoising an EMG signal for analysis, characteristics are extracted. It is the process of removing background noise from the original EMG signal data before isolating only the pertinent data. Separating relevant information from noise is necessary to improve the data. Finding the features that best describe the data is essential for improving classification performance for biomedical signal classification. By studying the traits of EMG signals from the muscles themselves, this endeavour seeks to more accurately categories neuromuscular diseases.
Keywords: EMG Signals, Feature Extraction and Denoising.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
