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