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document
Breaking it down is better : haptic decomposition of complex movements aids in robot-assisted motor learning . 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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Intervention_Psychological
haptic decomposition of complex movements
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Intervention_Educational
robot-assisted motor learning .
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Intervention_Psychological
Training with haptic guidance
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Intervention_Psychological
haptic feedback from a robotic exoskeleton
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Outcome_Other
motor learning .
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Outcome_Other
learning .
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Outcome_Physical
number of movements
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Outcome_Physical
target motion only
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Outcome_Physical
direct practice
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Outcome_Other
part training
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Outcome_Other
efficacy .
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Participant_Condition
unimpaired