Myoelectric Signal Segmentation and Classification Using Wavelets Based Neural Networks

Abstract

In this paper a method for Myoelectric signal (MES) segmentation and classification is proposed. The classical moving average technique augmented with Principal Components Analysis (PCA), and time- frequency analysis were used for segmentation. Multiresolution Wavelet Analysis (MRWA) was adopted as an effective feature extraction technique while Artificial Neural Networks (ANN) was used for MES classification. Results of classifying four elbow and wrist movements gave 94.9% sensitivity and 94.9% positive predictivity.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410317

Entities

People

  • Hasan Al-nashash
  • Yousef Al-assaf

Organizations

  • American University of Sharjah

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bioengineering
  • Biomechanical Phenomena
  • Biomedical Engineering
  • Character Recognition
  • Computer Vision
  • Data Analysis
  • Engineering
  • Feature Extraction
  • Frequency
  • Machine Learning
  • Medical Engineering
  • Neural Networks
  • Pattern Recognition
  • Prostheses And Implants
  • Signal Processing
  • Wavelet Transforms

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks