Article

Despite high classification accuracies (~95%) of myoelectric control systems
based on pattern recognition, how well offline measures translate to real-time
closed-loop control is unclear. Recently, a real-time virtual test analyzed how
well subjects completed arm motions using a multiple-degree of freedom (DOF)
classifier. Although this test provided real-time performance metrics, the
required task was oversimplified: motion speeds were normalized and unintended
movements were ignored. We included these considerations in a new, more
challenging virtual test called the Target Achievement Control Test (TAC Test).
Five subjects with transradial amputation attempted to move a virtual arm into a
target posture using myoelectric pattern recognition, performing the test with
various classifier (1- vs 3-DOF) and task complexities (one vs three required
motions per posture). We found no significant difference in classification
accuracy between the 1- and 3-DOF classifiers (97.2% +/- 2.0% and 94.1% +/- 3.1%,
respectively; p = 0.14). Subjects completed 31% fewer trials in significantly
more time using the 3-DOF classifier and took 3.6 +/- 0.8 times longer to reach a
three-motion posture compared with a one-motion posture. These results highlight
the need for closed-loop performance measures and demonstrate that the TAC Test
is a useful and more challenging tool to test real-time pattern-recognition
performance.

Langue : ANGLAIS