Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
557697 | Biomedical Signal Processing and Control | 2008 | 6 Pages |
Abstract
Pattern recognition based myoelectric control systems rely on detecting repeatable patterns at given electrode locations. This work describes an experiment to determine the effect of electrode displacements on pattern classification accuracy, and a classifier training strategy to accommodate this degradation. The results show that electrode displacements adversely affect classification accuracy, but training the system to recognize plausible displacement locations mitigates the effect. Furthermore, a combination of time-domain and autoregressive features appears to yield the best classification accuracy and is least affected by electrode displacements.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Levi Hargrove, Kevin Englehart, Bernard Hudgins,