The uncontrolled manifold (UCM) method is a well-established approach to assessing the coordination of multiple degrees of freedom (DoF) in synergies that stabilize performance in human actions. The method has been applied to a variety of actions, such as sit-to-stance, finger-force production, and
Inge Tuitert, Tim A. Valk, Egbert Otten, Laura Golenia, and Raoul M. Bongers
Christopher A. DiCesare, Scott Bonnette, Gregory D. Myer, and Adam W. Kiefer
), which may subsequently inform the quantification of such behavior during tasks often used in biomechanical-based injury risk assessments. One approach that quantifies synergistic behavior among motor system DOF is the uncontrolled manifold (UCM) analysis ( Scholz & Schoner, 1999 ). The UCM analysis is
Fariba Hasanbarani and Mark L. Latash
produced by abundant sets of elements has been developed within the framework of the uncontrolled manifold (UCM) hypothesis ( Scholz & Schöner, 1999 ; reviewed in Latash, Scholz, & Schöner, 2007 ). According to this concept, the highest, task-specific level of a hypothetical control hierarchy specifies
Mitchell Tillman and Satyajit Ambike
documented over the last decade. Synergies are systems that display task-specific covariation in redundant sets of inputs to ensure the stability of the output variables defining task performance ( Latash, Scholz, & Schoner, 2002 ). Synergies can be quantified using the uncontrolled manifold (UCM) method
Mark L. Latash
introduction of the uncontrolled manifold (UCM) hypothesis ( Scholz & Schöner, 1999 ; Schöner, 1995 ) and its associated computational apparatus for analysis of stability of potentially important performance variables in multidimensional spaces of elemental variables ( Latash et al., 2007 ). This breakthrough
David P. Black, Michael A. Riley, and Christopher K. McCord
The authors conducted two experiments that served as a test bed for applying the recently developed uncontrolled manifold (UCM) approach to rhythmic motor coordination, which has been extensively investigated from a coordination dynamics perspective. The results of two experiments, one investigating withinperson and one investigating between-persons rhythmic movement coordination, identified synergistic behaviors in both of those types of coordination. Stronger synergies were identified for in-phase than antiphase coordination, at the endpoints of the movement cycles compared with the midpoints, for movement frequencies closer to the intrinsic frequency of the coordinated limbs, and for within-person coordination. Frequency detuning did not weaken the strength of interlimb rhythmic coordination synergies. The results suggest the synergistic behavior captured by the UCM analysis may be identifiable with the strength of coupling between the coordinated limbs. The UCM analysis appears to distinguish coordination parameters that affect coupling strength from parameters that weaken coordination attractors.
Julien Jacquier-Bret, Nasser Rezzoug, and Philippe Gorce
In the presence of motor redundancy, recent studies have shown that goal equivalent configurations of the body segments might be used by the central nervous system (CNS) instead of stereotypical movement patterns. In particular, some authors have shown that the CNS might choose a subset of joint configurations (termed the uncontrolled manifold or UCM) such that variability (goal equivalent variance or GEV) in this subset does not affect the value of a particular performance variable while variability in the orthogonal subset ORT (non-goal equivalent variance or NGEV) does. This hypothesis has been used successfully to test whether specific performance variables such as endpoint trajectory or segment global orientation are stabilized by the CNS or to study the influence of constraints on the organization of the movement. Few studies have examined the redundancy problem when considering obstacle avoidance during a grasping task. Indeed, the majority of the works on this topic considers non redundant arm models or do not take into account the movement variability. In the present work, we sought to study the coordination of the trunk and the arm during a reaching task involving an obstacle and to test whether such a spatial constraint in extrinsic space may induce particular adaptations in term of joint flexibility when considering the shoulder, elbow, and wrist joint center positions. In this framework, the upper limb three-dimensional kinematics was recorded. From the calculated joint angles, the variability in joint space related to the three joint center positions was computed and decomposed into GEV and NGEV. In agreement with the UCM hypothesis, results showed higher values of GEV than NGEV for all the experimental conditions. The main finding of the study is that joints’ synergy is strengthened for the stabilization of the elbow joint center position during the late phases of the movement. This strengthening seems to be due mainly to an increase of GEV. Therefore, our results suggest that an increase of joint flexibility may be a mechanism by which the CNS takes into account a spatial constraint in extrinsic space represented by an obstacle.
Gregor Schöner and John P. Scholz
An important aspect of the study of multi-degree-of-freedom motor control is the analysis of high-dimensional variance data. Through the “uncontrolled manifold” (UCM) approach the structure in such data can be discovered and interpreted. The covariation by randomization (CR) approach provides nonlinear and potentially multi-dimensional measures of covariance. We critically examine these two approaches and compare them relative to the three fundamental issues of choice of variables, choice of model, and adoption of either a geometrical or a correlational view of variance. The UCM approach is a geometrical approach that seeks to discover the structure of variance in multi-degree-of-freedom task spaces in which all degrees of freedom have a common metric. The structure of variance in that space is interpreted in terms of its meaning for task variables. The CR approach seeks to uncover correlations between interpretable elemental variables. It requires a defined and common metric in the space of task variables, but not the elemental variables. Although the CR approach is better suited for systems with strong nonlinearities, variance structure that is not caused by correlation but by different amounts of variance in the different elemental variables is undetected by this approach.
Mark L. Latash
of control variables, such as RCs. This obviously requires defining such hypothetical spaces of control variables, which is a prerequisite for any study to claim that it addresses issues of motor control. The Principle of Abundance and the Uncontrolled Manifold Hypothesis Figure 1 may be viewed as
Mark L. Latash
eliminated. This is particularly puzzling given another very well known expression by Bernstein: “repetition without repetition”, implying variable means of performing actions over repetitive trials. The Principle of Abundance and the Uncontrolled Manifold Concept An alternative view at the problem of