HAND GESTURE RECOGNITION BASED ON EMG SIGNALS USING ANN
Objective
The objectives of this study is to Develop an artificial neural network (ANN) framework to accurately classify hand gestures based on electromyography (EMG) signals. Optimize feature extraction and signal processing techniques to enhance recognition performance and ensure real-time responsiveness. Validate the system through experimental evaluations and benchmarking against conventional methods to demonstrate improved accuracy and robustness.
Abstract
Using extracted time and amplitude threshold features, an Artificial Neural Network (ANN) is trained to diagnose patients using electromyography (EMG) signals. After preprocessing to eliminate noise, EMG signals are examined for important features such zero crossings, mean absolute value, peak amplitude, and signal length. An ANN model is trained using these variables to distinguish between normal and abnormal EMG patterns linked to neuromuscular diseases. The classification accuracy of the trained ANN is then assessed using fresh EMG data. Metrics including accuracy, sensitivity, specificity, and confusion matrix analysis are used in performance validation. ALS, muscular dystrophy, and nerve injury are among the muscle illnesses that can be accurately detected by a well-trained artificial neural network (ANN). Model dependability is increased by employing cross-validation and optimizing network parameters. This technique makes it possible to diagnose neuromuscular disorders effectively, automatically, and non-invasively.
Keywords: Electromyography, Artificial Neural Networks, Feature Extraction, Classification, Accuracy.
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 R2022b.
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 Math Works products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8
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 Communication?
· About Communication
· Introduction to Communication
· How Communication Works?
· Importing the System Design, Characterization and Visualization
· Analyzing of BER tool
· Analyzing of Error Rate Test Console
· Generation of WSN
· WSN network creation
· Nodes Communication
· Clustering
· Routing
· Convolutional
· Equalization and Synchronization 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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