Structure of motor variability

Report
STRUCTURE OF MOTOR
VARIABILITY
Kyung Koh
BACKGROUND
Motor variability
 A commonly seen features in human movements
 Bernstein “repetition without repetition”
In the past, motor variability is thought to be the result of error.
Scholz and Schöner (2002) developed the uncontrolled manifold analysis (UCM)
 Variability which creates error
 Variability which does not
MOTOR VARIABILITY
EXAMPLE – KINETIC VARIABLE
F1
F2
Task : F1 + F2 = 10N
(= a line equation [1D])


F1
= 

F2

F1
F2
+ error
F
+ 1
F2


=  + error
where
 
F1
F2

F1
F2



= 
= 
EXAMPLE – KINETIC VARIABLE
F2
Task : F1 + F2 = 10N
(= a line equation [1D])
F1 + F2 = 10N
10N
F1
 
F2

= 
VGood
Good variability
(which does not hurt performance)


F1
F2

= 
Bad Variability (which does)
VBad
10N
F1
UNCONTROLLED MANIFOLD ANALYSIS (UCM)
F2
 Task : F1 + F2 = 10N
(= a line equation [1D])
10N
Basis vector for UCM space
 Variability in a UCM space (task
irrelevant space)
 Variability in an orthogonal to
UCM space (task relevant space)
Basis vector
for a subspace orthogonal to UCM
10N
F1
UNCONTROLLED MANIFOLD ANALYSIS (UCM)
F3
 Task : F1 + F2 + F3 = 10N
(= a plane equation [2D])
10N
 Variability in a UCM space (task
irrelevant space)
Basis vectors for UCM space
 Variability in an orthogonal to
UCM space (task relevant space)
10N
F2
10N
F1
Basis vector
for orthogonal to UCM space
MOTOR SYNERGY
Uncontrolled Manifold Analysis (UCM) VS Principle Component Analysis (PCA)
F2
 A linear transformation that transforms
the data into a new coordinate system
(NCS)
10N
PCA coordinates
 A method to measure variance of the
data in NCS
UCM coordinates
10N
F1
EXAMPLE – KINEMATIC VARIABLE
Task : Target (Tx,Ty)

1 (1 , 2 , … ,7 )
= 
2 (1 , 2 , … ,7 )

By using jacobian Matraix,
1
1
2
1
1
2
2
2
⋯
⋯
1
7
2
7
1

2
=

⋮
7
∆ = ∆
∆ = ∆ + ∆ error
(∆ + ∆ ) = ∆ + ∆ error
where
∆ = ∆
∆ = ∆ error
MOTOR SYNERGIES
 Motor Synergies in UCM
Ratio of Vucm and Vorth are commonly used to measure synergies
STUDIES: MOTOR SYNERGIES
SUMMARY
There exists motor synergy
Task-specific co-variation of effectors with the purpose to stabilize a
performance variable (or minimize task error) (Latash 2002).
The CNS uses all the available DOFs to generate families of equivalent
solutions.
 DOFs work together to achieve a goal by compensating for each errors. (Gelfand and
Tsetlin 1967).
BENEFITS OF HAVING GREATER VARIABILITY IN
UCM
 Greater Variability in UCM space  The system is redundant.
 More DoFs than necessary to perform a particular task (e.g., F1 + F2 = 10N).
 During walking on an uneven surface, DOFs at the foot create variety of
configuration to maintain stability.
 Extra DOFs allows a system to be more flexible (e.g. when get injured)
24 DoF
1 DoF

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