Evaluating eMg Feature and classifier selection for application to Partial-hand Prosthesis control

Adenike A. Adewuyi*, Levi J. Hargrove, Todd A. Kuiken

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/ autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

Original languageEnglish (US)
Article number15
JournalFrontiers in Neurorobotics
StatePublished - 2016


  • Electromyography
  • Feature selection
  • Intrinsic hand muscles
  • Myoelectric control
  • Partial-hand amputee
  • Pattern recognition

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence


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