A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control.
Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of approximately 95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited movement speed when there was a change in classifier decision. The goal of this velocity ramp was to improve prosthesis positioning by minimizing the effect of unintended movements. Two patients who had undergone TMR surgery controlled either a virtual or physical prosthesis. They completed a Target Achievement Control Test where they commanded a virtual prosthesis into a target posture. Participants showed improved performance metrics of 34% increase in completion rate and 13% faster overall time with the velocity ramp compared to without the velocity ramp. One participant controlled a physical prosthesis and in three minutes was able to create a tower of 1" cubes seven blocks tall with the velocity ramp compared to a tower of only two blocks tall in the control condition. These results suggest that using a pattern recognition system with a decision based velocity profile may improve user performance.
Simon
Ann M
AM
Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. annie-simon@northwestern.edu
Hargrove
Levi J
LJ
Lock
Blair A
BA
Kuiken
Todd A
TA
D000465:Algorithms; D000674:Amputees; D001132:Arm; D001186:Artificial Limbs / Q000706:statistics & numerical data*; D001698:Biomedical Engineering; D003199:Computer Systems; D004576:Electromyography / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D010363:Pattern Recognition, Automated; D014584:User-Computer Interface