Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Muscles in the hands of the human are one of the important parts that depend on the performance of hand dutiest hrough movement which helps the man in the performance of daily functions which carry things and touch and feel everything around him. Muscle signals extracted by EMG system are used to diagnose muscle signals and classify them as normal or abnormal. The process of detecting and classifying EMG signals are difficult and exhausting process and require effort by the specialist doctor to diagnose them. Muscle injury treatment is complicated and require surgical intervention when the injury is severe but when early detection of injury may be treated without surgical intervention. In this study will use the EMG signalsthat have been collected as data. At the beginning extracting, the signals by tracking each signal. These signals are then analyze dusing DWT for muscle bands extraction that is used in feature extraction and using SVM for classification. DWT used for tofeature extraction.
How to cite this article:
Mahmood Khaleel Awsaj and Rabah Nory Farhan. Intelligent System for Electromyography (EMG) Signals Classification.
DOI: https://doi.org/10.36478/jeasci.2019.1564.1570
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.1564.1570