期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 20, 期 3, 页码 268-275出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2012.2195202
关键词
Haptic arm exoskeleton; motor learning; parallel mechanism; robot assisted movements; whole-part practice
资金
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) [N01-HD-3-3352]
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- NCRR [M01RR00827]
Training with haptic guidance has been proposed as a technique for learning complex movements in rehabilitation and sports, but it is unclear how to best deliver guidance-based training. Here, we hypothesized that breaking down a complex movement, similar to a tennis backhand, into simpler parts and then using haptic feedback from a robotic exoskeleton would help the motor system learn the movement. We also examined how the particular form of the decomposition affected learning. Three groups of unimpaired participants trained with the target arm movement broken down in three ways: 1) elbow flexion/extension and the unified shoulder motion independently (anatomical decomposition), 2) three component shoulder motions in Euler coordinates and elbow flexion/extension (Euler decomposition), or 3) the motion of the tip of the elbow and motion of the hand with respect to the elbow, independently (visual decomposition). A control group practiced the same number of movements, but experienced the target motion only, achieving eight times more direct practice with this motion. Despite less experience with the target motion, part training was better, but only when the arm trajectory was decomposed into anatomical components. Varying robotic movement training to include practice of simpler, anatomically-isolated motions may enhance its efficacy.
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