Humans use minimum cost movements in a whole-body task

Humans have elegant bodies that allow gymnastics, piano playing, and tool use, but understanding how they do this in detail is difficult because their musculoskeletal systems are extraordinarily complicated. Nonetheless, common movements like walking and reaching can be stereotypical, and a very large number of studies have shown their energetic cost to be a major factor. In contrast, one might think that general movements are very individuated and intractable, but our previous study has shown that in an arbitrary set of whole-body movements used to trace large-scale closed curves, near-identical posture sequences were chosen across different subjects, both in the average trajectories of the body’s limbs and in the variance within trajectories. The commonalities in that result motivate explanations for its generality. One explanation could be that humans also choose trajectories that are economical in cost. To test this hypothesis, we situate the tracing data within a forty eight degree of freedom human dynamic model that allows the computation of movement cost. Using the model to compare movement cost data from nominal tracings against various perturbed tracings shows that the latter are more energetically expensive, inferring that the original traces were chosen on the basis of minimum cost.

[1]  J. E. Cotes,et al.  The energy expenditure and mechanical energy demand in walking. , 1960 .

[2]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[3]  Rodolfo Margaria,et al.  Biomechanics and Energetics of Muscular Exercise , 1976 .

[4]  D. F. Hoyt,et al.  Gait and the energetics of locomotion in horses , 1981, Nature.

[5]  K. R. Williams,et al.  The effect of stride length variation on oxygen uptake during distance running. , 1982, Medicine and science in sports and exercise.

[6]  R. Burdett,et al.  Comparison of mechanical work and metabolic energy consumption during normal gait , 1983, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[7]  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.

[8]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[9]  Tsu-Tian Lee,et al.  Trajectory planning and control of a 3-link biped robot , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[10]  T. Flash,et al.  Arm Trajectory Modifications During Reaching Towards Visual Targets , 1991, Journal of Cognitive Neuroscience.

[11]  J. Hamill,et al.  Predicting the minimal energy costs of human walking. , 1991, Medicine and science in sports and exercise.

[12]  A. Minetti,et al.  Effects of stride frequency on mechanical power and energy expenditure of walking. , 1995, Medicine and science in sports and exercise.

[13]  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.

[14]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[15]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[16]  D. Wolpert Computational approaches to motor control , 1997, Trends in Cognitive Sciences.

[17]  R. Joseph,et al.  Fetal Brain Behavior and Cognitive Development , 2000 .

[18]  R. Kram,et al.  Mechanical and metabolic determinants of the preferred step width in human walking , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[19]  Tamar Flash,et al.  Computational approaches to motor control , 2001, Current Opinion in Neurobiology.

[20]  J. Donelan,et al.  Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. , 2002, The Journal of experimental biology.

[21]  M. Graziano,et al.  Complex Movements Evoked by Microstimulation of Precentral Cortex , 2002, Neuron.

[22]  Qiong Wu,et al.  Synthesis of a complete sagittal gait cycle for a five-link biped robot , 2003, Robotica.

[23]  Neville Hogan,et al.  The mechanics of multi-joint posture and movement control , 1985, Biological Cybernetics.

[24]  C. W. Radcliffe,et al.  Predicting metabolic cost of level walking , 1978, European Journal of Applied Physiology and Occupational Physiology.

[25]  E. Todorov Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[26]  H. Ralston,et al.  Optimization of energy expenditure during level walking , 2004, European Journal of Applied Physiology and Occupational Physiology.

[27]  Qiong Wu,et al.  Sagittal gait synthesis for a five-link biped robot , 2004, Proceedings of the 2004 American Control Conference.

[28]  W. L. Nelson Physical principles for economies of skilled movements , 1983, Biological Cybernetics.

[29]  H. Ralston Energy-speed relation and optimal speed during level walking , 1958, Internationale Zeitschrift für angewandte Physiologie einschließlich Arbeitsphysiologie.

[30]  Michael I. Jordan,et al.  Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study , 1995, Experimental Brain Research.

[31]  Xiuping Mu Dynamics and motion regulation of a five-link biped robot walking in the sagittal plane , 2005 .

[32]  Philip E. Martin,et al.  Mechanical power and efficiency of level walking with different stride rates , 2007, Journal of Experimental Biology.

[33]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[34]  Ahmad Bagheri,et al.  Mathematical simulation of a seven link biped robot on various surfaces and ZMP considerations , 2007 .

[35]  Alexander P. Krishchenko,et al.  Planar walking control for a five-link biped robot , 2007 .

[36]  Eli Brenner,et al.  Similarities between digits’ movements in grasping, touching and pushing , 2010, Experimental Brain Research.

[37]  Christine Chevallereau,et al.  3D Bipedal Robotic Walking: Models, Feedback Control, and Open Problems , 2010 .

[38]  Matthew T. Kaufman,et al.  Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine? , 2010, Neuron.

[39]  Christopher J. Arellano,et al.  The effects of step width and arm swing on energetic cost and lateral balance during running. , 2011, Journal of biomechanics.

[40]  Jeffrey A Reinbolt,et al.  OpenSim: a musculoskeletal modeling and simulation framework for in silico investigations and exchange. , 2011, Procedia IUTAM.

[41]  Marc Jeannerod,et al.  Hand aperture patterns in prehension. , 2012, Human movement science.

[42]  Yuval Tassa,et al.  MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[43]  Helen J. Huang,et al.  Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics , 2012, The Journal of Neuroscience.

[44]  Dana H. Ballard,et al.  Realtime, Physics-Based Marker Following , 2012, MIG.

[45]  Joseph L. Cooper,et al.  Analysis and synthesis of bipedal humanoid movement : a physical simulation approach , 2013 .

[46]  Romain Hérault,et al.  Climbing skill and complexity of climbing wall design: assessment of jerk as a novel indicator of performance fluency. , 2014, Journal of applied biomechanics.

[47]  Dana H. Ballard,et al.  Efficient Codes for Inverse Dynamics During Walking , 2014, AAAI.

[48]  Daniel Ludvig,et al.  Task-relevant adaptation of musculoskeletal impedance during posture and movement , 2014, 2014 American Control Conference.

[49]  Parth Rajesh Desai,et al.  A Review Paper on Oculus Rift-A Virtual Reality Headset , 2014, ArXiv.

[50]  Evgeni Magid,et al.  3D Modelling of Biped Robot Locomotion with Walking Primitives Approach in Simulink Environment , 2015, ICINCO.

[51]  Jessica C. Selinger,et al.  Humans Can Continuously Optimize Energetic Cost during Walking , 2015, Current Biology.

[52]  Yuval Tassa,et al.  Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[53]  G. Pazour,et al.  Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.

[54]  Bastien Berret,et al.  Optimality and Modularity in Human Movement: From Optimal Control to Muscle Synergies , 2018, Biomechanics of Anthropomorphic Systems.

[55]  Xhevahir Bajrami,et al.  KINEMATIC MODEL OF THE SEVEN-LINK BIPED ROBOT , 2017 .

[56]  James M. Finley,et al.  Evidence of Energetic Optimization during Adaptation Differs for Metabolic, Mechanical, and Perceptual Estimates of Energetic Cost , 2017, Scientific Reports.

[57]  A. V. van Soest,et al.  Inverse dynamics of mechanical multibody systems: An improved algorithm that ensures consistency between kinematics and external forces , 2018, PloS one.

[58]  Alaa A. Ahmed,et al.  Vigor of reaching movements: reward discounts the cost of effort. , 2018, Journal of neurophysiology.

[59]  Sergej Čelikovský,et al.  Stable walking gaits for a three-link planar biped robot with two actuators based on the collocated virtual holonomic constraints and the cyclic unactuated variable , 2018 .

[60]  Jessica C. Selinger,et al.  Is natural variability in gait sufficient to initiate spontaneous energy optimization in human walking? , 2019, Journal of neurophysiology.

[61]  Dana H Ballard,et al.  Humans Use Similar Posture Sequences in a Whole-Body Tracing Task , 2019, iScience.

[62]  Scott L. Delp,et al.  OpenSim Moco: Musculoskeletal optimal control , 2019, bioRxiv.

[63]  Christopher L. Dembia,et al.  OpenSim Moco: Musculoskeletal optimal control , 2020, PLoS computational biology.

[64]  Reza Shadmehr,et al.  Vigor , 2020 .

[65]  Dana H. Ballard,et al.  Computational Modeling: Human Dynamic Model , 2021, Frontiers in Neurorobotics.