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Force field adaptation can be learned using vision in the absence of proprioceptive error

Force field adaptation can be learned using vision in the absence of proprioceptive error. A. Melendez-Calderon, L. Masia, R. Gassert, G. Sandini, E. Burdet Motor Control Reading Group Michele Rotella August 30, 2013. Ideal vs. Constrained Movement. Ideal robotic trainer (6 DOF)

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Force field adaptation can be learned using vision in the absence of proprioceptive error

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  1. Force field adaptation can be learned using vision in the absence of proprioceptive error A. Melendez-Calderon, L. Masia, R. Gassert, G. Sandini, E. Burdet Motor Control Reading Group Michele Rotella August 30, 2013

  2. Ideal vs. Constrained Movement • Ideal robotic trainer (6 DOF) • Realistic movements • BUT, complex, bulky, not portable • Safety • Reduced DOF trainer • Cheaper, simpler, mobile • BUT, lost information, different dynamics • Will transfer to complex movement? Exo-UL3

  3. Research Question! Can performance gains in a constrained environment transfer to an unconstrained (real movement) environment? If mechanical constraints limits arm movement, can visionreplace proprioceptiveinformation in learning new arm dynamics?

  4. Integration of Sensory Modes Vision Proprioception Importance ?

  5. Experiment: targeted reaches Braccio di Ferro • Subjects • 30, right-handed • Device • 2 DOF planar manipulandum • General task • Control cursor with handle position • Perform point-to-point movements • Successful reach to target in 0.6 ± 0.1 s • Color feedback on speed • Single (Exp. 1) or five (Exp. 2) movement directions

  6. Experiment Environments • Null force field (NF) • No force, visual feedback of robot/hand position • Viscous curl force field (VF) • Velocity dependent force field, visual feedback • Virtual null force field (vNF), vision ≠ proprioception • Stiff haptic channel • Measure lateral force  estimate movement (robot + arm dynamics) • Visual feedback actual arm + lateral deviation • Virtual viscous force field (vVF) • Stiff haptic channel • Measure lateral force  estimate movement (robot + arm dynamics) • Estimate velocity of arm  estimate viscous curl field • Visual feedback actual arm +viscous curl field deviation

  7. Real Environment

  8. Virtual Environment World Frame Target Frame Real Virtual

  9. Experimental Protocols • Exp. 1: Unidirectional force field learning • Exp. 2: Multidirectional force field learning

  10. Data Analysis & Expected Results • Performance metrics • Feed-forward control: Aiming error at 150 ms • Directional Error: Aiming error at 300 ms • Between-group analysis • Pearson’s correlation coefficient between mean trajectories • T-tests between groups • Hypothesis • Over time, directional error decreases, catch trial error increases • Similar trajectories for vVF and VF

  11. Results: Unidirectional Learning Similar Full Washout/ Baseline Gradually Straighten Opposite Similar Slower Large oscillations

  12. Results: Unidirectional Learning(cont.) Feedforward Component Curvature & Lateral Deviation Smaller for uVG *Subjects are not aware of the constraining channel

  13. Results: Multidirectional Learning Similar paths indicate learning of vVF * All paths highly correlated

  14. Results: Multidirectional Learning(cont.) * Per target, more time to learn single target than many target directions Difference in beginning (Incomplete learning) Smaller in virtual environment

  15. Discussion • Can learn new dynamics without proprioceptive error • Visual feedback shows arm dynamics • Uni- vs. multidirectional task • Unidirectional – no difference between uVG and UCG • Multidirectional – different aftereffects, incomplete learning • Transfer of learning in a virtual environ. to real movement • But, some proprioception + force feedback from channel • Maybe the CNS favored visual information over proprioception based on reliability

  16. Applications • Sport training • Complex movements with simple (take-home) devices • Rehabilitation • Simple devices, safer, cheaper • Stroke patients have impaired feed-forward control • Create visual feedback that could correct lateral forces

  17. Thoughts… • Direct connection to our isometric studies! • We totally constrain movement • Consider a visual perturbation • We use simple dynamics that do not necessarily represent the arm • How realistic do the virtual dynamics have to be for training? • Actual arm dynamics? • How much error in the arm model? • Virtual dynamics of another system?

  18. Thoughts… • Why could subjects not tell when their arm was constrained? • How would results change if people could see their hand? • How can we manipulate how much someone relies on a certain type of feedback? • This has come up before! • Why did the required reaching length change between uni- and multi-directional experiments?

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