A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

Background Recently, muscle synergy analysis has become a standard methodology for extracting coordination patterns from electromyographic (EMG) signals, and for the evaluation of motor control strategies in many contexts. Most previous studies have characterized upper-limb muscle synergies across a limited set of reaching movements. With the aim of future uses in motor control, rehabilitation and other fields, this study provides a comprehensive characterization of muscle synergies in a large set of upper-limb tasks and also considers inter-individual and environmental variability. Methods Sixteen healthy subjects performed upper-limb hand exploration movements for a comprehensive mapping of the upper-limb workspace, which was divided into several sectors (Frontal, Right, Left, Horizontal, and Up). EMGs from representative upper-limb muscles and kinematics were recorded to extract muscle synergies and explore the composition, repeatability and similarity of spatial synergies across subjects and movement directions, in a context of high variability of motion. Results Even in a context of high variability, a reduced set of muscle synergies may reconstruct the original EMG envelopes. Composition, repeatability and similarity of synergies were found to be shared across subjects and sectors, even if at a lower extent than previously reported. Conclusion Extending the results of previous studies, which were performed on a smaller set of conditions, a limited number of muscle synergies underlie the execution of a large variety of upper-limb tasks. However, the considered spatial domain and the variability seem to influence the number and composition of muscle synergies. Such detailed characterization of the modular organization of the muscle patterns for upper-limb control in a large variety of tasks may provide a useful reference for studies on motor control, rehabilitation, industrial applications, and sports.

[1]  Benjamin J. Fregly,et al.  Methodological Choices in Muscle Synergy Analysis Impact Differentiation of Physiological Characteristics Following Stroke , 2017, Front. Comput. Neurosci..

[2]  Loredana Zollo,et al.  Assessment of Muscular Activation Patterns in 3D Upper Limb Robot-Aided Rehabilitation , 2018 .

[3]  Dario Farina,et al.  Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles , 2014, Front. Hum. Neurosci..

[4]  Matthew C. Tresch,et al.  The number and choice of muscles impact the results of muscle synergy analyses , 2013, Front. Comput. Neurosci..

[5]  J. F. Soechting,et al.  Early stages in a sensorimotor transformation , 1992, Behavioral and Brain Sciences.

[6]  Valentina Squeri,et al.  Desirable features of a “humanoid” robot-therapist , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Alessandro Scano,et al.  Low-Cost Tracking Systems Allow Fine Biomechanical Evaluation of Upper-Limb Daily-Life Gestures in Healthy People and Post-Stroke Patients , 2019, Sensors.

[8]  E. Bizzi,et al.  Muscle synergy patterns as physiological markers of motor cortical damage , 2012, Proceedings of the National Academy of Sciences.

[9]  Kang He,et al.  The Statistical Determinants of the Speed of Motor Learning , 2016, PLoS Comput. Biol..

[10]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[11]  Ashesh K Dhawale,et al.  The Role of Variability in Motor Learning. , 2017, Annual review of neuroscience.

[12]  E. Bizzi,et al.  Article history: , 2005 .

[13]  N. Stergiou,et al.  Human movement variability, nonlinear dynamics, and pathology: is there a connection? , 2011, Human movement science.

[14]  Yasuharu Koike,et al.  When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance , 2019, bioRxiv.

[15]  Xiang Chen,et al.  Muscle synergy analysis for similar upper limb motion tasks , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Rajiv Ranganathan,et al.  High variability impairs motor learning regardless of whether it affects task performance , 2017, bioRxiv.

[17]  Stefano Panzeri,et al.  Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives , 2013, Front. Comput. Neurosci..

[18]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[19]  Seyed A Safavynia,et al.  Long-latency muscle activity reflects continuous, delayed sensorimotor feedback of task-level and not joint-level error. , 2013, Journal of neurophysiology.

[20]  Stefano Panzeri,et al.  Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements , 2018, Scientific Reports.

[21]  A. d’Avella,et al.  A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability , 2019, bioRxiv.

[22]  S. Micera,et al.  Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: a pilot study on healthy subjects , 2016, Journal of NeuroEngineering and Rehabilitation.

[23]  Anis Sahbani,et al.  Analysis of hand synergies in healthy subjects during bimanual manipulation of various objects , 2014, Journal of NeuroEngineering and Rehabilitation.

[24]  Emanuel Todorov,et al.  Structured variability of muscle activations supports the minimal intervention principle of motor control. , 2009, Journal of neurophysiology.

[25]  Silvestro Micera,et al.  Effects of early and intensive neuro-rehabilitative treatment on muscle synergies in acute post-stroke patients: a pilot study , 2013, Journal of NeuroEngineering and Rehabilitation.

[26]  Jinsook Roh,et al.  Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment , 2015, Front. Hum. Neurosci..

[27]  Yohsuke R. Miyamoto,et al.  Temporal structure of motor variability is dynamically regulated and predicts motor learning ability , 2014, Nature Neuroscience.

[28]  Jessica L. Allen,et al.  Neuromechanical Principles Underlying Movement Modularity and Their Implications for Rehabilitation , 2015, Neuron.

[29]  W. Rymer,et al.  Alterations in upper limb muscle synergy structure in chronic stroke survivors. , 2013, Journal of neurophysiology.

[30]  Alessandro Scano,et al.  Assessing User Transparency with Muscle Synergies during Exoskeleton-Assisted Movements: A Pilot Study on the LIGHTarm Device for Neurorehabilitation , 2018, Applied bionics and biomechanics.

[31]  Mark L. Latash,et al.  The bliss (not the problem) of motor abundance (not redundancy) , 2012, Experimental Brain Research.

[32]  F. Lacquaniti,et al.  Five basic muscle activation patterns account for muscle activity during human locomotion , 2004, The Journal of physiology.

[33]  E. Bizzi,et al.  Stability of muscle synergies for voluntary actions after cortical stroke in humans , 2009, Proceedings of the National Academy of Sciences.

[34]  Giovanni Pezzulo,et al.  Evidence for sparse synergies in grasping actions , 2018, Scientific Reports.

[35]  A. d’Avella,et al.  Locomotor Primitives in Newborn Babies and Their Development , 2011, Science.

[36]  Alessandra Pedrocchi,et al.  Robustness and Reliability of Synergy-Based Myocontrol of a Multiple Degree of Freedom Robotic Arm , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[37]  Stefano Panzeri,et al.  Quantitative evaluation of muscle synergy models: a single-trial task decoding approach , 2013, Front. Comput. Neurosci..

[38]  Emilio Bizzi,et al.  The neural origin of muscle synergies , 2013, Front. Comput. Neurosci..

[39]  B. Freriks,et al.  Development of recommendations for SEMG sensors and sensor placement procedures. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[40]  Andrea d'Avella,et al.  Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. , 2006, Journal of neurophysiology.

[41]  M. Latash,et al.  Motor Control Strategies Revealed in the Structure of Motor Variability , 2002, Exercise and sport sciences reviews.

[42]  Valentina Agostini,et al.  Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review , 2018, Applied bionics and biomechanics.

[43]  Alessandro Scano,et al.  Robotic Assistance for Upper Limbs May Induce Slight Changes in Motor Modules Compared With Free Movements in Stroke Survivors: A Cluster-Based Muscle Synergy Analysis , 2018, Front. Hum. Neurosci..

[44]  Silvestro Micera,et al.  The effect of arm weight support on upper limb muscle synergies during reaching movements , 2014, Journal of NeuroEngineering and Rehabilitation.

[45]  Francesco Lacquaniti,et al.  Control of Fast-Reaching Movements by Muscle Synergy Combinations , 2006, The Journal of Neuroscience.

[46]  Cristina Becchio,et al.  Decoding intentions from movement kinematics , 2016, Scientific Reports.

[47]  L. Ting,et al.  Muscle synergies characterizing human postural responses. , 2007, Journal of neurophysiology.

[48]  Egbert Otten,et al.  Synergies reciprocally relate end-effector and joint-angles in rhythmic pointing movements , 2019, Scientific Reports.

[49]  Francesco Lacquaniti,et al.  Modulation of phasic and tonic muscle synergies with reaching direction and speed. , 2008, Journal of neurophysiology.

[50]  F. Molteni,et al.  Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements , 2017, Front. Bioeng. Biotechnol..

[51]  S. Micera,et al.  How are Muscle Synergies Affected by Electromyography Pre-Processing? , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[52]  Bastien Berret,et al.  Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions , 2017, bioRxiv.

[53]  Richard R Neptune,et al.  Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. , 2010, Journal of neurophysiology.

[54]  Francesco Lacquaniti,et al.  Dimensionality of joint torques and muscle patterns for reaching , 2014, Front. Comput. Neurosci..

[55]  E. Bizzi,et al.  Are Modular Activations Altered in Lower Limb Muscles of Persons with Multiple Sclerosis during Walking? Evidence from Muscle Synergies and Biomechanical Analysis , 2016, Front. Hum. Neurosci..