User Training for Pattern Recognition-Based Myoelectric Prostheses: Improving Phantom Limb Movement Consistency and Distinguishability

We assessed the ability of four transradial amputees to control a virtual prosthesis capable of nine classes of movement both before and after a two-week training period. Subjects attended eight one-on-one training sessions that focused on improving the consistency and distinguishability of their ha...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2014-05, Vol.22 (3), p.522-532
Main Authors: Powell, Michael A., Kaliki, Rahul R., Thakor, Nitish V.
Format: Article
Language:English
Subjects:
Adult
Algorithms
Amputation
Artificial Intelligence
Electrodes
Electromyography (EMG)
Electromyography - methods
Female
Forearm - physiology
Humans
Male
Middle Aged
motor learning
Movement - physiology
Pattern recognition
Pattern Recognition, Automated - methods
Phantom Limb - rehabilitation
Phantoms
Prostheses
Prosthesis Design - methods
Prosthetics
rehabilitation
Reliability
therapy
Training
Wrist
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Summary:We assessed the ability of four transradial amputees to control a virtual prosthesis capable of nine classes of movement both before and after a two-week training period. Subjects attended eight one-on-one training sessions that focused on improving the consistency and distinguishability of their hand and wrist movements using visual biofeedback from a virtual prosthesis. The virtual environment facilitated the precise quantification of three prosthesis control measures. During a final evaluation, the subject population saw an average increase in movement completion percentage from 70.8% to 99.0%, an average improvement in normalized movement completion time from 1.47 to 1.13, and an average increase in movement classifier accuracy from 77.5% to 94.4% (p
ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2013.2279737