期刊
JOURNAL OF REHABILITATION RESEARCH AND DEVELOPMENT
卷 48, 期 6, 页码 619-627出版社
JOURNAL REHAB RES & DEV
DOI: 10.1682/JRRD.2010.08.0149
关键词
multifunctional prosthesis; myoelectric control; pattern recognition; performance test; proportional control; prosthesis; surface electromyography; transradial amputation; upper limb; virtual environment
资金
- National Institutes of Health, National Institute of Child Health and Human Development [R01 HD058000-01, R01 HD043137-05]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [R01HD058000] Funding Source: NIH RePORTER
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT [R01HD043137] Funding Source: NIH RePORTER
Despite high classification accuracies (similar to 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 (DOE) 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 ill 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.
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