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Inge Tuitert, Tim A. Valk, Egbert Otten, Laura Golenia, and Raoul M. Bongers

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

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Francis M. Grover, Valéria Andrade, Nicole S. Carver, Scott Bonnette, Michael A. Riley, and Paula L. Silva

Manifold Method The uncontrolled manifold (UCM) method was developed to partition motor variability into compensatory and uncompensatory variability metrics in order to investigate and quantify the presence of a synergy ( Scholz & Schöner, 1999 ). The UCM analysis generates a synergy index by quantifying

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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

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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

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Matthew Slopecki, Fariba Hasanbarani, Chen Yang, Christopher A. Bailey, and Julie N. Côté

 al., 2011 ), no changes in intersegmental variability were observed for the fatigued repetitive pointing task (RPT) ( Yang et al., 2019 ). However, they did not analyze how these adaptations impacted task performance, a topic of investigation that can be made using the uncontrolled manifold (UCM) hypothesis

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Mark L. Latash

begins with a brief review of the theory of control with RCs and the principle of abundance, implemented using the framework of the uncontrolled manifold (UCM) hypothesis ( Scholz & Schöner, 1999 ; Schöner, 1995 ). Furthermore, we have introduced explicit definitions for three movement characteristics

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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

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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

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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.

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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.