Robustness of Using Dynamic Motions and Template Matching to the Limb Position Effect in Myoelectric Classification

Sungtae Shin, Reza Tafreshi, Reza Langari

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Myoelectric classification has been widely studied for controlling prosthetic devices and human computer interface (HCI). However, it is still not robust due to external conditions: limb position changes, electrode shifts, and skin condition changes. These issues compromise the reliability of pattern recognition techniques in myoelectric systems. In order to increase the reliability in the limb position effect when a limb position is changed from the position in which the system is trained, this paper proposes a myoelectric system using dynamic motions. Dynamic time warping (DTW) technique was used for the alignment of two different time-length motions, and correlation coefficients were then calculated as a similarity metric to classify dynamic motions. On the other hand, Fisher's linear discriminant analysis was applied on static motions for the purpose of dimensionality reduction and Naïve Bayesian classifier for classifying the motions. To estimate the robustness to the limb position effect, static and dynamic motions were collected at four different limb positions from eight human subjects. The statistical analysis, t-test (p < 0.05), showed that, for all subjects, dynamic motions were more robust to the limb position effect than static motions when training and validation sets were extracted from different limb positions with the best classification accuracy of 97.59% and 3.54% standard deviation (SD) for dynamic motions compared with 71.85% with 12.62% SD for static motions.

Original languageEnglish
Article number111009
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume138
Issue number11
DOIs
Publication statusPublished - 1 Nov 2016

Fingerprint

Template matching
limbs
templates
Discriminant analysis
Prosthetics
Interfaces (computer)
Pattern recognition
standard deviation
Statistical methods
Skin
Dynamical systems
prosthetic devices
Classifiers
human-computer interface
Electrodes
classifiers
classifying
correlation coefficients
pattern recognition
statistical analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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abstract = "Myoelectric classification has been widely studied for controlling prosthetic devices and human computer interface (HCI). However, it is still not robust due to external conditions: limb position changes, electrode shifts, and skin condition changes. These issues compromise the reliability of pattern recognition techniques in myoelectric systems. In order to increase the reliability in the limb position effect when a limb position is changed from the position in which the system is trained, this paper proposes a myoelectric system using dynamic motions. Dynamic time warping (DTW) technique was used for the alignment of two different time-length motions, and correlation coefficients were then calculated as a similarity metric to classify dynamic motions. On the other hand, Fisher's linear discriminant analysis was applied on static motions for the purpose of dimensionality reduction and Na{\"i}ve Bayesian classifier for classifying the motions. To estimate the robustness to the limb position effect, static and dynamic motions were collected at four different limb positions from eight human subjects. The statistical analysis, t-test (p < 0.05), showed that, for all subjects, dynamic motions were more robust to the limb position effect than static motions when training and validation sets were extracted from different limb positions with the best classification accuracy of 97.59{\%} and 3.54{\%} standard deviation (SD) for dynamic motions compared with 71.85{\%} with 12.62{\%} SD for static motions.",
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