Complex Upper-Limb Movements Are Generated by Combining Motor Primitives that Scale with the Movement Size

The hand trajectory of motion during the performance of one-dimensional point-to-point movements has been shown to be marked by motor primitives with a bell-shaped velocity profile. Researchers have investigated if motor primitives with the same shape mark also complex upper-limb movements. They have done so by analyzing the magnitude of the hand trajectory velocity vector. This approach has failed to identify motor primitives with a bell-shaped velocity profile as the basic elements underlying the generation of complex upper-limb movements. In this study, we examined upper-limb movements by analyzing instead the movement components defined according to a Cartesian coordinate system with axes oriented in the medio-lateral, antero-posterior, and vertical directions. To our surprise, we found out that a broad set of complex upper-limb movements can be modeled as a combination of motor primitives with a bell-shaped velocity profile defined according to the axes of the above-defined coordinate system. Most notably, we discovered that these motor primitives scale with the size of movement according to a power law. These results provide a novel key to the interpretation of brain and muscle synergy studies suggesting that human subjects use a scale-invariant encoding of movement patterns when performing upper-limb movements.

[1]  Adrian M. Haith,et al.  Motor planning flexibly optimizes performance under uncertainty about task goals , 2017, Nature Communications.

[2]  Tamar Flash,et al.  Stopping is not an option: the evolution of unstoppable motion elements (primitives). , 2015, Journal of neurophysiology.

[3]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[4]  Bruce Hoff,et al.  A model of duration in normal and perturbed reaching movement , 1994, Biological Cybernetics.

[5]  P. Viviani,et al.  The law relating the kinematic and figural aspects of drawing movements. , 1983, Acta psychologica.

[6]  Emanuel Todorov,et al.  Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control , 2007, The Journal of Neuroscience.

[7]  John W. Krakauer,et al.  Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision , 2015, PLoS Comput. Biol..

[8]  T. Flash,et al.  Riemannian geometric approach to human arm dynamics, movement optimization, and invariance. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  N. Hogan,et al.  Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.

[10]  G. Stelmach,et al.  Movement accuracy constraints in Parkinson’s disease patients , 2000, Neuropsychologia.

[11]  Masaya Hirashima,et al.  Prospective errors determine motor learning , 2015, Nature Communications.

[12]  Tamar Flash,et al.  Affine differential geometry analysis of human arm movements , 2007, Biological Cybernetics.

[13]  P. Viviani,et al.  The relation between linear extent and velocity in drawing movements , 1983, Neuroscience.

[14]  Daniel Bennequin,et al.  Geometrical Invariance and Smoothness Maximization for Task-Space Movement Generation , 2016, IEEE Transactions on Robotics.

[15]  R. Shadmehr,et al.  Temporal Discounting of Reward and the Cost of Time in Motor Control , 2010, The Journal of Neuroscience.

[16]  P Viviani,et al.  Segmentation and coupling in complex movements. , 1985, Journal of experimental psychology. Human perception and performance.

[17]  F. Jean,et al.  Why Don't We Move Slower? The Value of Time in the Neural Control of Action , 2016, The Journal of Neuroscience.

[18]  Alain Berthoz,et al.  Complex unconstrained three-dimensional hand movement and constant equi-affine speed. , 2009, Journal of neurophysiology.

[19]  Lionel Rigoux,et al.  A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control , 2012, PLoS Comput. Biol..

[20]  T. Flash,et al.  Scale-Invariant Movement Encoding in the Human Motor System , 2014, Neuron.

[21]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[22]  E Thelen,et al.  Development of reaching during the first year: role of movement speed. , 1996, Journal of experimental psychology. Human perception and performance.

[23]  Terrence J Sejnowski,et al.  Spectrum of power laws for curved hand movements , 2015, Proceedings of the National Academy of Sciences.

[24]  J. S. Thomas,et al.  The Continuity of Movements , 1973 .

[25]  M. Landy,et al.  Movement planning with probabilistic target information. , 2007, Journal of neurophysiology.

[26]  W. L. Bryan On the Development of Voluntary Motor Ability , 1892 .

[27]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[28]  P. Fitts,et al.  Information capacity of discrete motor responses under different cognitive sets. , 1966, Journal of experimental psychology.

[29]  G. Stelmach,et al.  Parkinsonism Reduces Coordination of Fingers, Wrist, and Arm in Fine Motor Control , 1997, Experimental Neurology.

[30]  T. Pohlert The Pairwise Multiple Comparison of Mean Ranks Package (PMCMR) , 2016 .

[31]  Michael S. Landy,et al.  Sinusoidal error perturbation reveals multiple coordinate systems for sensorymotor adaptation , 2016, Vision Research.

[32]  Dylan F. Cooke,et al.  The Cortical Control of Movement Revisited , 2002, Neuron.

[33]  T. Flash,et al.  Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. , 1995, Journal of experimental psychology. Human perception and performance.

[34]  R. H. Stetson,et al.  Mechanism of the different types of movement. , 1923 .

[35]  Yoram Ben-Shaul,et al.  A Compact Representation of Drawing Movements with Sequences of Parabolic Primitives , 2009, PLoS Comput. Biol..

[36]  Michael S Landy,et al.  Motor learning reveals the existence of multiple codes for movement planning. , 2012, Journal of neurophysiology.

[37]  N. Berthier Learning to reach: A mathematical model. , 1996 .

[38]  A. Wing,et al.  Relation between velocity and curvature in movement: equivalence and divergence between a power law and a minimum-jerk model. , 1988, Journal of experimental psychology. Human perception and performance.

[39]  Vernon B. Brooks,et al.  Introductory lecture to session III some examples of programmed limb movements , 1974 .

[40]  Eran Stark,et al.  Parabolic movement primitives and cortical states: merging optimality with geometric invariance , 2009, Biological Cybernetics.

[41]  Howard G. Wu,et al.  The Generalization of Visuomotor Learning to Untrained Movements and Movement Sequences Based on Movement Vector and Goal Location Remapping , 2013, The Journal of Neuroscience.

[42]  T. Flash,et al.  Comparing Smooth Arm Movements with the Two-Thirds Power Law and the Related Segmented-Control Hypothesis , 2002, The Journal of Neuroscience.

[43]  N. Dubin Mathematical Model , 2022 .

[44]  V B Brooks,et al.  Some examples of programmed limb movements. , 1974, Brain research.

[45]  Simon A. Overduin,et al.  Modulation of Muscle Synergy Recruitment in Primate Grasping , 2008, The Journal of Neuroscience.

[46]  Emilio Bizzi,et al.  Representation of Muscle Synergies in the Primate Brain , 2015, The Journal of Neuroscience.

[47]  P. Fitts,et al.  INFORMATION CAPACITY OF DISCRETE MOTOR RESPONSES. , 1964, Journal of experimental psychology.

[48]  N. Hogan,et al.  Quantization of continuous arm movements in humans with brain injury. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[49]  G. Sapiro,et al.  Constant Affine Velocity Predicts the 1 3 Power Law of Planar Motion Perception and Generation , 1997, Vision Research.

[50]  P. Viviani,et al.  Trajectory determines movement dynamics , 1982, Neuroscience.