Adapting myoelectric control in real-time using a virtual environment.

Published on Jan 16, 2019in Journal of Neuroengineering and Rehabilitation3.58
· DOI :10.1186/s12984-019-0480-5
Richard B. Woodward1
Estimated H-index: 1
(NU: Northwestern University),
Levi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University)
Background Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.
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