Motion normalized proportional control for improved pattern recognition-based myoelectric control

Erik Scheme, Blair Lock, Levi Hargrove, Wendy Hill, Usha Kuruganti, Kevin Englehart

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method of deriving proportional control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects (p < 0.001) and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput (p < 0.0001), yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed control schemes represent a computationally simple method of fundamentally improving myoelectric control users' ability to elicit robust, and controlled, proportional velocity commands.

Original languageEnglish (US)
Article number1
Pages (from-to)149-157
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number1
StatePublished - Jan 2014


  • amputee
  • electromyogram (EMG)
  • myoelectric
  • pattern recognition
  • proportional control
  • prostheses
  • velocity control

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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