title: Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses.
abstract: 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.
authors.affiliation: Center for Bionic Medicine, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. asimon@ric.org
authors.forename: Ann M
authors.initials: AM
authors.lastname: Simon
authors.forename: Levi J
authors.initials: LJ
authors.lastname: Hargrove
authors.forename: Blair A
authors.initials: BA
authors.lastname: Lock
authors.forename: Todd A
authors.initials: TA
authors.lastname: Kuiken
mesh_terms: D000328:Adult; D000671:Amputation, Surgical / Q000534:rehabilitation*; D001132:Arm; D001186:Artificial Limbs; D006801:Humans; D008875:Middle Aged; D009068:Movement; D058765:Neurofeedback; D010363:Pattern Recognition, Automated; D011474:Prosthesis Design; D013647:Task Performance and Analysis; D013997:Time Factors; D055815:Young Adult