An exploration of the optimal feature-classifier combinations for transradial prosthesis control

Fraser Douglas, Harry Gover, Cheryl Docherty, Gordon Shields, Konstantina Leventi, Gaetano Di Caterina

Research output: Contribution to conferencePaperpeer-review

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Within state-of-the-art gesture-based upper-limb myoelectric prosthesis control, gesture recognition commonly relies on the classification of features extracted from electromygraphic (EMG) data gathered from the amputee's residual forearm musculature. Despite best efforts in broadly maximizing gesture recognition accuracy, there does not yet exist a feature-classifier combination accepted as best-practice. In turn, this work hypothesizes that no single feature-classifier combination can consistently maximize accuracy across subjects, positing instead that control schemes should be personalized to the individual. To investigate this hypothesis, the study employed the 40-subject, 49- gesture Ninapro DB2 to compare the performance of 7 different historic, more recent and state-of-the-art feature sets, in combination with 5 machine learning classifiers commonly seen within EMG-based pattern recognition literature. The results demonstrate the ability of Linear Discriminant Analysis (LDA) to marginally exceed other more computationally intensive classifiers in terms of mean accuracy, while the feature set which maximized the highest proportion of individuals' accuracies was shown to vary with both classifier choice and gesture count.
Original languageEnglish
Number of pages4
Publication statusPublished - 15 Jul 2022
EventEngineering in Medicine and Biology Conference - Glasgow, United Kingdom
Duration: 11 Jul 202215 Jul 2022
Conference number: 2022


ConferenceEngineering in Medicine and Biology Conference
Abbreviated titleEMBC
Country/TerritoryUnited Kingdom
Internet address


  • exploration
  • optimal feature-classifier combinations
  • transradial prosthesis control


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