Loading...

Introducing a novel SEMG ANN-based regression approach for elbow motion interpolation

Karbasi, H ; Sharif University of Technology | 2019

386 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/CCOMS.2019.8821733
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Surface electromyogram (sEMG) signals are extensively used for rehabilitation and control purposes. However due to their intrinsic complexities and intense sensor crosstalk, feature classification and pattern recognition of sEMG signals especially for motion analysis are quite challenging. This study proposes a versatile sEMG Artificial Neural Network based regression approach to evaluate a simple elbow motion with respect to a reference frame. The proposed approach attempts to appropriately interpolate intermediate position angles in an attempt to evaluate and substantiate a continuous motion of the forearm. Results show that based on the proposed algorithm, with a correlation of about 91% for the test data, it is possible to track the motion of the forearm through a set of discrete sEMG signals. © 2019 IEEE
  6. Keywords:
  7. ANN ; Continuous movement ; Feature extraction ; Interpolation ; Regression ; Semg ; Neural networks ; Pattern recognition ; Regression analysis ; Continuous motions ; Feature classification ; Motion interpolation ; Reference frame ; Surface electromyogram ; Biomedical signal processing
  8. Source: 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019, 23 February 2019 through 25 February 2019 ; 2019 , Pages 77-80 ; 9781728113227 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/8821733