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 vision replace proprioceptive information in learning new arm dynamics?

4. Integration of Sensory Modes
Used successfully by deafferented patients
Type influences performance
Necessary (or not) for force-field or dynamic learning
Dynamics learned in muscle space
Influences speed of learning
Not necessary to learn object manipulation task
Vision
Proprioception
Importance ?

5. Experiment: targeted reaches
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
Braccio di Ferro

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)
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
Fam.
Learning
Testing I
Testing II
Washout
Post-washout
uVG(10)
Virtual,
Constrained
vNF (25)
vVF (150)
vVF(20),
VF(5)
Catch Trials,
Learning Effect
NF(5)
After Effect
NF(25)
NF(20),
uCG(10)
Unconstrained
NF (25)
VF (150) NA
VF(20),
Fam.
Learning
Testing I
Testing II
Washout
Post-washout
mVG(5)
vNF (10)
vVF (30)
vVF(20),
VF(5)
NF(5)
NF(10)
NF(20),
mCG(5)
NF (10)
VF (30) NA
VF(20),

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
6, rely only on visual feedback and no knowledge of pertrubation
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 Rehabilitation
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?