Publications by authors named "Francisco J Valero-Cuevas"

82 Publications

Data-efficient Causal Decoding of Spiking Neural Activity using Weighted Voting.

Annu Int Conf IEEE Eng Med Biol Soc 2021 11;2021:5850-5855

Brain-Computer Interface systems can contribute to a vast set of applications such as overcoming physical disabilities in people with neural injuries or hands-free control of devices in healthy individuals. However, having systems that can accurately interpret intention online remains a challenge in this field. Robust and data-efficient decoding-despite the dynamical nature of cortical activity and causality requirements for physical function-is among the most important challenges that limit the widespread use of these devices for real-world applications. Here, we present a causal, data-efficient neural decoding pipeline that predicts intention by first classifying recordings in short sliding windows. Next, it performs weighted voting over initial predictions up to the current point in time to report a refined final prediction. We demonstrate its utility by classifying spiking neural activity collected from the human posterior parietal cortex for a cue, delay, imaginary motor task. This pipeline provides higher classification accuracy than state-of-the-art time windowed spiking activity based causal methods, and is robust to the choice of hyper-parameters.
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http://dx.doi.org/10.1109/EMBC46164.2021.9631022DOI Listing
November 2021

Estimating Center of Pressure of a Bipedal Mechanism Using a Proprioceptive Artificial Skin around its Ankles.

Annu Int Conf IEEE Eng Med Biol Soc 2021 11;2021:4522-4528

Estimating the Center of Pressure (CoP) under legged robots is useful to control their posture and gait. This is traditionally done using contact sensors at the base of the foot or with sensors on distal joints, which are subject to wear and damage due to impulse forces. In vertebrates, skin and ligament deformation at the ankle is a particularly rich source of sensory information for locomotion. For our bipedal mechanism, afferent signals from sensors on synthetic skin wrapped around the ankles sufficed to estimate the location of the CoP with a mean accuracy >81.5%. For this we used K-Nearest Neighbors (KNN) algorithm trained on the same force magnitude applied at four and nine ground-truth CoP locations. For a single mechanical foot (i.e., single stance), signals from skin or ligaments (i.e., elastic rubber sheets and cables, respectively) also sufficed to calculate the CoP (Mean prediction accuracy >91.3%). Moreover, the visco-elasticity of these elements serves to passively stabilize the ankle. Importantly, training the single leg case with forces of different magnitudes also resulted in similarly accurate mean CoP prediction accuracy >84.5%. We show that using bio-inspired proprioceptive skins and/or ligament arrangements can provide reliable COP predictions, while permitting arbitrary postures of the ankle and no sensors on the sole of the foot prone to wear and damage. This novel approach to estimation of the CoP can be used to improve locomotion control in a new class of bio-inspired rigid, soft and hybrid (soft-rigid) legged robots.
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http://dx.doi.org/10.1109/EMBC46164.2021.9630631DOI Listing
November 2021

: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons.

Front Neurorobot 2021 11;15:679122. Epub 2021 Oct 11.

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.

Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where () motors are not placed at the joints and () nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum-antagonistically-driven by motors and nonlinearly-elastic tendons-we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.
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http://dx.doi.org/10.3389/fnbot.2021.679122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542795PMC
October 2021

Temporal control of muscle synergies is linked with alpha-band neural drive.

J Physiol 2021 07 31;599(13):3385-3402. Epub 2021 May 31.

Department of Computer Science, University of Southern California, Los Angeles, CA, USA.

Key Points: It is theorized that the nervous system controls groups of muscles together as functional units, or 'synergies', resulting in correlated electromyographic (EMG) signals among muscles. However, such correlation does not necessarily imply group-level neural control. Oscillatory synchronization (coherence) among EMG signals implies neural coupling, but it is not clear how this relates to control of muscle synergies. EMG was recorded from seven arm muscles of 10 adult participants rotating an upper limb ergometer, and EMG-EMG coherence, EMG amplitude correlations and their relationship with each other were characterized. A novel method to derive multi-muscle synergies from EMG-EMG coherence is presented and these are compared with classically defined synergies. Coherent alpha-band (8-16 Hz) drive was strongest among muscles whose gross activity levels are well correlated within a given task. The cross-muscle distribution and temporal modulation of coherent alpha-band drive suggests a possible role in the neural coordination/monitoring of synergies.

Abstract: During movement, groups of muscles may be controlled together by the nervous system as an adaptable functional entity, or 'synergy'. The rules governing when (or if) this occurs during voluntary behaviour in humans are not well understood, at least in part because synergies are usually defined by correlated patterns of muscle activity without regard for the underlying structure of their neural control. In this study, we investigated the extent to which comodulation of muscle output (i.e. correlation of electromyographic (EMG) amplitudes) implies that muscles share intermuscular neural input (assessed via EMG-EMG coherence analysis). We first examined this relationship among pairs of upper limb muscles engaged in an arm cycling task. We then applied a novel multidimensional EMG-EMG coherence analysis allowing synergies to be characterized on the basis of shared neural drive. We found that alpha-band coherence (8-16 Hz) is related to the degree to which overall muscle activity levels correlate over time. The extension of this coherence analysis to describe the cross-muscle distribution and temporal modulation of alpha-band drive revealed a close match to the temporal and structural features of traditionally defined muscle synergies. Interestingly, the coherence-derived neural drive was inversely associated with, and preceded, changes in EMG amplitudes by ∼200 ms. Our novel characterization of how alpha-band neural drive is dynamically distributed among muscles is a fundamental step forward in understanding the neural origins and correlates of muscle synergies.
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http://dx.doi.org/10.1113/JP281232DOI Listing
July 2021

Force variability is mostly not motor noise: Theoretical implications for motor control.

PLoS Comput Biol 2021 03 8;17(3):e1008707. Epub 2021 Mar 8.

Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California, United States of America.

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.
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http://dx.doi.org/10.1371/journal.pcbi.1008707DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971898PMC
March 2021

Sampling-Based Nonlinear Stochastic Optimal Control for Neuromechanical Systems.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:4694-4699

Determining how the nervous system controls tendon-driven bodies remains an open question. Stochastic optimal control (SOC) has been proposed as a plausible analogy in the neuroscience community. SOC relies on solving the Hamilton-Jacobi-Bellman equation, which seeks to minimize a desired cost function for a given task with noisy controls. We evaluate and compare three SOC methodologies to produce tapping by a simulated planar 3-joint human index finger: iterative Linear Quadratic Gaussian (iLQG), Model-Predictive Path Integral Control (MPPI), and Deep Forward-Backward Stochastic Differential Equations (FBSDE). We show that averaged over 128 repeats these methodologies can place the fingertip at the desired final joint angles but-because of kinematic redundancy and the presence of noise-they each have joint trajectories and final postures with different means and variances. iLQG in particular, had the largest kinematic variance and departure from the final desired joint angles. We demonstrate that MPPI and FBSDE have superior performance for such nonlinear, tendon-driven systems with noisy controls.Clinical relevance- The mathematical framework provided by MPPI and FBSDE may be best suited for tendon-driven anthropomorphic robots, exoskeletons, and prostheses for amputees.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175861DOI Listing
July 2020

Parkinson's Disease Exhibits Amplified Intermuscular Coherence During Dynamic Voluntary Action.

Front Neurol 2020 3;11:204. Epub 2020 Apr 3.

Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States.

Parkinson's disease (PD) is typically diagnosed and evaluated on the basis of overt motor dysfunction, however, subtle changes in the frequency spectrum of neural drive to muscles have been reported as well. During dynamic actions, coactive muscles of healthy adults often share a common source of 6-15 Hz (alpha-band) neural drive, creating synchronous alpha-band activity in their EMG signals. Individuals with PD commonly exhibit kinetic action tremor at similar frequencies, but the potential relationship between the intermuscular alpha-band neural drive seen in healthy adults and the action tremor associated with PD is not well-understood. A close relationship is most tenable during voluntary dynamic tasks where alpha-band neural drive is strongest in healthy adults, and where neural circuits affected by PD are most engaged. In this study, we characterized the frequency spectrum of EMG synchronization (intermuscular coherence) in 16 participants with PD and 15 age-matched controls during two dynamic motor tasks: (1) rotation of a dial between the thumb and index finger, and (2) dynamic scaling of isometric precision pinch force. These tasks produce different profiles of coherence between the first dorsal interosseous and abductor pollicis brevis muscles. We sought to determine if alpha-band intermuscular coherence would be amplified in participants with PD relative to controls, if such differences would be task-specific, and if they would correlate with symptom severity. We found that relative to controls, the PD group displayed amplified, but similarly task-dependent, coherence in the alpha-band. The magnitude of coherence during the rotation task correlated with overall symptom severity as per the UPDRS rating scale. Finally, we explored the potential for our coherence measures, with no additional information, to discriminate individuals with PD from controls. The area under the Receiver Operating Characteristic curve (AUC) indicated a clear separation between groups (AUC = 0.96), even though participants with PD were on their typical medication and displayed only mild-moderate symptoms. We conclude that a task-dependent, intermuscular neural drive within the alpha-band is amplified in PD. Its quantification via intermuscular coherence analysis may provide a useful tool for detecting the presence of PD, or assessing its progression.
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http://dx.doi.org/10.3389/fneur.2020.00204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145888PMC
April 2020

Autonomous Functional Movements in a Tendon-Driven Limb via Limited Experience.

Nat Mach Intell 2019 Mar 11;1(3):144-154. Epub 2019 Mar 11.

Department of Biomedical, University of Southern California, Los Angeles, CA, USA.

Robots will become ubiquitously useful only when they can use few attempts to teach themselves to perform different tasks, even with complex bodies and in dynamical environments. Vertebrates, in fact, use sparse trial-and-error to learn multiple tasks despite their intricate tendon-driven anatomies-which are particularly hard to control because they are simultaneously nonlinear, under-determined, and over-determined. We demonstrate-for the first time in simulation and hardware-how a model-free, open-loop approach allows few-shot autonomous learning to produce effective movements in a 3-tendon 2-joint limb. We use a short period of motor babbling (to create an initial inverse map) followed by building functional habits by reinforcing high-reward behavior and refinements of the inverse map in a movement's neighborhood. This biologically-plausible algorithm, which we call G2P (General-to-Particular), can potentially enable quick, robust and versatile adaptation in robots as well as shed light on the foundations of the enviable functional versatility of organisms.
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http://dx.doi.org/10.1038/s42256-019-0029-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544439PMC
March 2019

An Analytical Approach to Posture-Dependent Muscle Force and Muscle Activation Patterns.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:2068-2071

Personalized training by taking into account individual anatomy to improve performance is a research frontier. In this paper, we first introduce an analytical method to study the pattern of changes in muscle forces as a function of posture. Our method is also able to analyze variation of maximal muscle force and muscle activation values (in various postures) as a result of posture-dependent changes in moment arms. This method also helps us evaluate the utility of person specific training. It also provides us with model based approximations for activation and muscle force patterns during different motions without a need for subject recordings, which enables athletes to have a better understanding of how each muscle contributes during each posture, in a fast and efficient way. Second, we analyze the results of this method for a simple squat move. Our results show that both maximal muscle force and muscle activation values have variable sensitivity to the moment arm values for different postures and muscles. It suggests that individually modified training plans could likely improve performance for some sets of movements.
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http://dx.doi.org/10.1109/EMBC.2018.8512607DOI Listing
July 2018

Model-Free Control of Movement in a Tendon-Driven Limb via a Modified Genetic Algorithm.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:1767-1770

Tendon-driven systems have many benefits over other actuation strategies such as torque-driven systems; however, their over-determined nature and posture-dependent actuation presents strong constraints on their control. Also, parameters or even exact structure of the model in these systems, especially in the biological ones, are normally not clear to the controller. Here, we propose a modified Genetic Algorithm that provides the tendon excursion values for the limb to follow a desired trajectory. Our results show that the proposed algorithm was able to accurately follow the desired trajectory without the model of the system being exposed to it. We believe that this method can enable biologically inspired tendon-driven mechanisms with variable mechanical structures to autonomously control their movements.
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http://dx.doi.org/10.1109/EMBC.2018.8512616DOI Listing
July 2018

Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control.

Front Comput Neurosci 2018 11;12:62. Epub 2018 Sep 11.

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.

We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its 'feasible activation space'). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes-in a complete way-the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D. The feasible activation space for a given task is landscape where all neuromuscular learning, control, and performance must occur. This approach unifies current theories of neuromuscular control because the structure of feasible activation spaces can be separately approximated as either low-dimensional basis functions (synergies), high-dimensional joint probability distributions (Bayesian priors), or fitness landscapes (to optimize cost functions).
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http://dx.doi.org/10.3389/fncom.2018.00062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141757PMC
September 2018

A Physical Model Suggests That Hip-Localized Balance Sense in Birds Improves State Estimation in Perching: Implications for Bipedal Robots.

Front Robot AI 2018 4;5:38. Epub 2018 Apr 4.

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.

In addition to a vestibular system, birds uniquely have a balance-sensing organ within the pelvis, called the lumbosacral organ (LSO). The LSO is well developed in terrestrial birds, possibly to facilitate balance control in perching and terrestrial locomotion. No previous studies have quantified the functional benefits of the LSO for balance. We suggest two main benefits of hip-localized balance sense: reduced sensorimotor delay and improved estimation of foot-ground acceleration. We used system identification to test the hypothesis that hip-localized balance sense improves estimates of foot acceleration compared to a head-localized sense, due to closer proximity to the feet. We built a physical model of a standing guinea fowl perched on a platform, and used 3D accelerometers at the hip and head to replicate balance sense by the LSO and vestibular systems. The horizontal platform was attached to the end effector of a 6 DOF robotic arm, allowing us to apply perturbations to the platform analogous to motions of a compliant branch. We also compared state estimation between models with low and high neck stiffness. Cross-correlations revealed that foot-to-hip sensing delays were shorter than foot-to-head, as expected. We used multi-variable output error state-space (MOESP) system identification to estimate foot-ground acceleration as a function of hip- and head-localized sensing, individually and combined. Hip-localized sensors alone provided the best state estimates, which were not improved when fused with head-localized sensors. However, estimates from head-localized sensors improved with higher neck stiffness. Our findings support the hypothesis that hip-localized balance sense improves the speed and accuracy of foot state estimation compared to head-localized sense. The findings also suggest a role of neck muscles for active sensing for balance control: increased neck stiffness through muscle co-contraction can improve the utility of vestibular signals. Our engineering approach provides, to our knowledge, the first quantitative evidence for functional benefits of the LSO balance sense in birds. The findings support notions of control modularity in birds, with preferential vestibular sense for head stability and gaze, and LSO for body balance control,respectively. The findings also suggest advantages for distributed and active sensing for agile locomotion in compliant bipedal robots.
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http://dx.doi.org/10.3389/frobt.2018.00038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806032PMC
April 2018

Cardinal features of involuntary force variability can arise from the closed-loop control of viscoelastic afferented muscles.

PLoS Comput Biol 2018 01 8;14(1):e1005884. Epub 2018 Jan 8.

Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California, United States of America.

Involuntary force variability below 15 Hz arises from, and is influenced by, many factors including descending neural drive, proprioceptive feedback, and mechanical properties of muscles and tendons. However, their potential interactions that give rise to the well-structured spectrum of involuntary force variability are not well understood due to a lack of experimental techniques. Here, we investigated the generation, modulation, and interactions among different sources of force variability using a physiologically-grounded closed-loop simulation of an afferented muscle model. The closed-loop simulation included a musculotendon model, muscle spindle, Golgi tendon organ (GTO), and a tracking controller which enabled target-guided force tracking. We demonstrate that closed-loop control of an afferented musculotendon suffices to replicate and explain surprisingly many cardinal features of involuntary force variability. Specifically, we present 1) a potential origin of low-frequency force variability associated with co-modulation of motor unit firing rates (i.e.,'common drive'), 2) an in-depth characterization of how proprioceptive feedback pathways suffice to generate 5-12 Hz physiological tremor, and 3) evidence that modulation of those feedback pathways (i.e., presynaptic inhibition of Ia and Ib afferents, and spindle sensitivity via fusimotor drive) influence the full spectrum of force variability. These results highlight the previously underestimated importance of closed-loop neuromechanical interactions in explaining involuntary force variability during voluntary 'isometric' force control. Furthermore, these results provide the basis for a unifying theory that relates spinal circuitry to various manifestations of altered involuntary force variability in fatigue, aging and neurological disease.
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http://dx.doi.org/10.1371/journal.pcbi.1005884DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5774830PMC
January 2018

Finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:986-989

Electrocardiogram (ECoG) recordings are very attractive for Brain Machine Interface (BMI) applications due to their balance between good signal to noise ratio and minimal invasiveness. The design of ECoG signal decoders is an open research area to date which requires a better understanding of the nature of these signals and how information is encoded in them. In this study, a linear and a non-linear method, Linear Regression Model (LRM) and Artificial Neural Network (ANN) respectively, were used to decode finger movements from energy in band-specific ECoG signals. It is shown that the ANN only slightly outperformed the LRM, which suggests that finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals. In addition, comparing our results to similar Electroencephalogram (EEG) studies illustrated that the spatio-temporal summation of multiple neural signals is itself linearly correlated with movement, and is not an artifact introduced by the scalp or cranium. Furthermore, a new algorithm was employed to reduce the number of spectral features of the input signals required for either of the decoding methods.
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http://dx.doi.org/10.1109/EMBC.2017.8036991DOI Listing
July 2017

Physiological tremor increases when skeletal muscle is shortened: implications for fusimotor control.

J Physiol 2017 12 19;595(24):7331-7346. Epub 2017 Nov 19.

Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA.

Key Points: In tonic, isometric, plantarflexion contractions, physiological tremor increases as the ankle joint becomes plantarflexed. Modulation of physiological tremor as a function of muscle stretch differs from that of the stretch reflex amplitude. Amplitude of physiological tremor may be altered as a function of reflex pathway gains. Healthy humans likely increase their γ-static fusimotor drive when muscles shorten. Quantification of physiological tremor by manipulation of joint angle may be a useful experimental probe of afferent gains and/or the integrity of automatic fusimotor control.

Abstract: The involuntary force fluctuations associated with physiological (as distinct from pathological) tremor are an unavoidable component of human motor control. While the origins of physiological tremor are known to depend on muscle afferentation, it is possible that the mechanical properties of muscle-tendon systems also affect its generation, amplification and maintenance. In this paper, we investigated the dependence of physiological tremor on muscle length in healthy individuals. We measured physiological tremor during tonic, isometric plantarflexion torque at 30% of maximum at three ankle angles. The amplitude of physiological tremor increased as calf muscles shortened in contrast to the stretch reflex whose amplitude decreases as muscle shortens. We used a published closed-loop simulation model of afferented muscle to explore the mechanisms responsible for this behaviour. We demonstrate that changing muscle lengths does not suffice to explain our experimental findings. Rather, the model consistently required the modulation of  γ-static fusimotor drive to produce increases in physiological tremor with muscle shortening - while successfully replicating the concomitant reduction in stretch reflex amplitude. This need to control γ-static fusimotor drive explicitly as a function of muscle length has important implications. First, it permits the amplitudes of physiological tremor and stretch reflex to be decoupled. Second, it postulates neuromechanical interactions that require length-dependent γ drive modulation to be independent from α drive to the parent muscle. Lastly, it suggests that physiological tremor can be used as a simple, non-invasive measure of the afferent mechanisms underlying healthy motor function, and their disruption in neurological conditions.
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http://dx.doi.org/10.1113/JP274899DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5730841PMC
December 2017

On neuromechanical approaches for the study of biological and robotic grasp and manipulation.

J Neuroeng Rehabil 2017 10 9;14(1):101. Epub 2017 Oct 9.

School of Biological and Health Systems Engineering Arizona State University, Tempe, AZ, USA.

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.
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http://dx.doi.org/10.1186/s12984-017-0305-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635506PMC
October 2017

Sex differences in leg dexterity are not present in elite athletes.

J Biomech 2017 10 14;63:1-7. Epub 2017 Sep 14.

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA. Electronic address:

We studied whether the time-varying forces that control unstable foot-ground interactions provide insight into the neural control of dynamic leg function. Twenty elite (10F, 26.4±3.5yrs) and 20 recreational (10F, 24.8±2.4yrs) athletes used an isolated leg to maximally compress a slender spring designed to buckle at low forces while seated. The foot forces during the compression at the edge of instability quantify the maximal sensorimotor ability to control dynamic foot-ground interactions. Using the nonlinear analysis technique of attractor reconstruction, we characterized the spatial (interquartile range IQR) and geometric (trajectory length TL, volume V, and sum of edge lengths SE) features of the dynamical behavior of those force time series. ANOVA confirmed the already published effect of sex, and a new effect of athletic ability, respectively, in TL (p=0.014 and p<0.001), IQR (p=0.008 and p<0.001), V (p=0.034 and p=0.002), and SE (p=0.033 and p<0.001). Further analysis revealed that, for recreational athletes, females exhibited weaker corrective actions and greater stochasticity than males as per their greater mean values of TL (p=0.003), IQR (p=0.018), V (p=0.017), and SE (p=0.025). Importantly, sex differences disappeared in elite athletes. These results provide an empirical link between sex, athletic ability, and nonlinear dynamical control. This is a first step in understanding the sensorimotor mechanisms for control of unstable foot-ground interactions. Given that females suffer a greater incidence of non-contact knee ligament injuries, these non-invasive and practical metrics of leg dexterity may be both indicators of athletic ability, and predictors of risk of injury.
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http://dx.doi.org/10.1016/j.jbiomech.2017.09.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679466PMC
October 2017

Intermuscular coherence reflects functional coordination.

J Neurophysiol 2017 09 28;118(3):1775-1783. Epub 2017 Jun 28.

Brain-Body Dynamics Laboratory, Department of Biomedical Engineering, Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California

Coherence analysis has the ability to identify the presence of common descending drive shared by motor unit pools and reveals its spectral properties. However, the link between spectral properties of shared neural drive and functional interactions among muscles remains unclear. We assessed shared neural drive between muscles of the thumb and index finger while participants executed two mechanically distinct precision pinch tasks, each requiring distinct functional coordination among muscles. We found that shared neural drive was systematically reduced or enhanced at specific frequencies of interest (~10 and ~40 Hz). While amplitude correlations between surface EMG signals also exhibited changes across tasks, only their coherence has strong physiological underpinnings indicative of neural binding. Our results support the use of intermuscular coherence as a tool to detect when coactivated muscles are members of a functional group or synergy of neural origin. Furthermore, our results demonstrate the advantages of considering neural binding at 10, ~20, and >30 Hz, as indicators of task-dependent neural coordination strategies. It is often unclear whether correlated activity among muscles reflects their neural binding or simply reflects the constraints defining the task. Using the fact that high-frequency coherence between EMG signals (>6 Hz) is thought to reflect shared neural drive, we demonstrate that coherence analysis can reveal the neural origin of distinct muscle coordination patterns required by different tasks.
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http://dx.doi.org/10.1152/jn.00204.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596118PMC
September 2017

Similar movements are associated with drastically different muscle contraction velocities.

J Biomech 2017 07 31;59:90-100. Epub 2017 May 31.

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA. Electronic address:

We investigated how kinematic redundancy interacts with the neurophysiological control mechanisms required for smooth and accurate, rapid limb movements. Biomechanically speaking, tendon excursions are over-determined because the rotation of few joints determines the lengths and velocities of many muscles. But how different are the muscle velocity profiles induced by various, equally valid hand trajectories? We used an 18-muscle sagittal-plane arm model to calculate 100,000 feasible shoulder, elbow, and wrist joint rotations that produced valid basketball free throws with different hand trajectories, but identical initial and final hand positions and velocities. We found large differences in the eccentric and concentric muscle velocity profiles across many trajectories; even among similar trajectories. These differences have important consequences to their neural control because each trajectory will require unique, time-sensitive reflex modulation strategies. As Sherrington mentioned a century ago, failure to appropriately silence the stretch reflex of any one eccentrically contracting muscle will disrupt movement. Thus, trajectories that produce faster or more variable eccentric contractions will require more precise timing of reflex modulation across motoneuron pools; resulting in higher sensitivity to time delays, muscle mechanics, excitation/contraction dynamics, noise, errors and perturbations. By combining fundamental concepts of biomechanics and neuroscience, we propose that kinematic and muscle redundancy are, in fact, severely limited by the need to regulate reflex mechanisms in a task-specific and time-critical way. This in turn has important consequences to the learning and execution of accurate, smooth and repeatable movements-and to the rehabilitation of everyday limb movements in developmental and neurological conditions, and stroke.
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http://dx.doi.org/10.1016/j.jbiomech.2017.05.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5541912PMC
July 2017

Forearm Flexor Muscles in Children with Cerebral Palsy Are Weak, Thin and Stiff.

Front Comput Neurosci 2017 25;11:30. Epub 2017 Apr 25.

Department of Women's and Children's Health, Karolinska InstituteStockholm, Sweden.

Children with cerebral palsy (CP) often develop reduced passive range of motion with age. The determining factor underlying this process is believed to be progressive development of contracture in skeletal muscle that likely changes the biomechanics of the joints. Consequently, to identify the underlying mechanisms, we modeled the mechanical characteristics of the forearm flexors acting across the wrist joint. We investigated skeletal muscle strength (Grippit®) and passive stiffness and viscosity of the forearm flexors in 15 typically developing (TD) children (10 boys/5 girls, mean age 12 years, range 8-18 yrs) and nine children with CP Nine children (6 boys/3 girls, mean age 11 ± 3 years (yrs), range 7-15 yrs) using the NeuroFlexor® apparatus. The muscle stiffness we estimate and report is the instantaneous mechanical response of the tissue that is independent of reflex activity. Furthermore, we assessed cross-sectional area of the flexor carpi radialis (FCR) muscle using ultrasound. Age and body weight did not differ significantly between the two groups. Children with CP had a significantly weaker (-65%, < 0.01) grip and had smaller cross-sectional area (-43%, < 0.01) of the FCR muscle. Passive stiffness of the forearm muscles in children with CP was increased 2-fold ( < 0.05) whereas viscosity did not differ significantly between CP and TD children. FCR cross-sectional area correlated to age ( = 0.58, < 0.01), body weight ( = 0.92, < 0.0001) and grip strength ( = 0.82, < 0.0001) in TD children but only to grip strength ( = 0.60, < 0.05) in children with CP. We conclude that children with CP have weaker, thinner, and stiffer forearm flexors as compared to typically developing children.
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http://dx.doi.org/10.3389/fncom.2017.00030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5403928PMC
April 2017

Beta Band Corticomuscular Drive Reflects Muscle Coordination Strategies.

Front Comput Neurosci 2017 4;11:17. Epub 2017 Apr 4.

Brain-Body Dynamics Lab, Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA.

During force production, hand muscle activity is known to be coherent with activity in primary motor cortex, specifically in the beta-band (15-30 Hz) frequency range. It is not clear, however, if this coherence reflects the control strategy selected by the nervous system for a given task, or if it instead reflects an intrinsic property of cortico-spinal communication. Here, we measured corticomuscular and intermuscular coherence between muscles of index finger and thumb while a two-finger pinch grip of identical net force was applied to objects which were either stable (allowing synergistic activation of finger muscles) or unstable (requiring individuated finger control). We found that beta-band corticomuscular coherence with the first (FDI) and (APB) muscles, as well as their beta-band coherence with each other, was significantly reduced when individuated control of the thumb and index finger was required. We interpret these findings to show that beta-band coherence is reflective of a synergistic control strategy in which the cortex binds task-related motor neurons into functional units.
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http://dx.doi.org/10.3389/fncom.2017.00017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378725PMC
April 2017

Characterization of the disruption of neural control strategies for dynamic fingertip forces from attractor reconstruction.

PLoS One 2017 13;12(2):e0172025. Epub 2017 Feb 13.

Brain-Body Dynamics Laboratory, Department of Biomedical Engineering & Division of Biokinesiology and Physical Therapy, University of Southern California, 3710 McClintock Ave., Los Angeles, CA, 90089, United States of America.

The Strength-Dexterity (SD) test measures the ability of the pulps of the thumb and index finger to compress a compliant and slender spring prone to buckling at low forces (<3N). We know that factors such as aging and neurodegenerative conditions bring deteriorating physiological changes (e.g., at the level of motor cortex, cerebellum, and basal ganglia), which lead to an overall loss of dexterous ability. However, little is known about how these changes reflect upon the dynamics of the underlying biological system. The spring-hand system exhibits nonlinear dynamical behavior and here we characterize the dynamical behavior of the phase portraits using attractor reconstruction. Thirty participants performed the SD test: 10 young adults, 10 older adults, and 10 older adults with Parkinson's disease (PD). We used delayed embedding of the applied force to reconstruct its attractor. We characterized the distribution of points of the phase portraits by their density (number of distant points and interquartile range) and geometric features (trajectory length and size). We find phase portraits from older adults exhibit more distant points (p = 0.028) than young adults and participants with PD have larger interquartile ranges (p = 0.001), trajectory lengths (p = 0.005), and size (p = 0.003) than their healthy counterparts. The increased size of the phase portraits with healthy aging suggests a change in the dynamical properties of the system, which may represent a weakening of the neural control strategy. In contrast, the distortion of the attractor in PD suggests a fundamental change in the underlying biological system, and disruption of the neural control strategy. This ability to detect differences in the biological mechanisms of dexterity in healthy and pathological aging provides a simple means to assess their disruption in neurodegenerative conditions and justifies further studies to understand the link with the physiological changes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172025PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5305200PMC
August 2017

Neuromorphic meets neuromechanics, part II: the role of fusimotor drive.

J Neural Eng 2017 04 17;14(2):025002. Epub 2017 Jan 17.

Division of Biokinesiology and Physical Therapy, University of Southern California, CA, United States of America.

Objective: We studied the fundamentals of muscle afferentation by building a Neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of the effects of different fusimotor drives to the muscle spindle on the closed-loop stretch reflex response.

Approach: As in Part I of this work, sensory neurons conveyed proprioceptive information from muscle spindles (with static and dynamic fusimotor drive) to populations of α-motor neurons (with recruitment and rate coding properties). The motor commands were transformed into tendon forces by a Hill-type muscle model (with activation-contraction dynamics) via brushless DC motors. Two independent afferented muscles emulated the forces of flexor digitorum profundus and the extensor indicis proprius muscles, forming an antagonist pair at the metacarpophalangeal joint of a cadaveric index finger. We measured the physical response to repetitions of bi-directional ramp-and-hold rotational perturbations for 81 combinations of static and dynamic fusimotor drives, across four ramp velocities, and three levels of constant cortical drive to the α-motor neuron pool.

Main Results: We found that this system produced responses compatible with the physiological literature. Fusimotor and cortical drives had nonlinear effects on the reflex forces. In particular, only cortical drive affected the sensitivity of reflex forces to static fusimotor drive. In contrast, both static fusimotor and cortical drives reduced the sensitivity to dynamic fusimotor drive. Interestingly, realistic signal-dependent motor noise emerged naturally in our system without having been explicitly modeled.

Significance: We demonstrate that these fundamental features of spinal afferentation sufficed to produce muscle function. As such, our Neuro-mechano-morphic system is a viable platform to study the spinal mechanisms for healthy muscle function-and its pathologies such as dystonia and spasticity. In addition, it is a working prototype of a robust biomorphic controller for compliant robotic limbs and exoskeletons.
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http://dx.doi.org/10.1088/1741-2552/aa59bdDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394229PMC
April 2017

Neuromorphic meets neuromechanics, part I: the methodology and implementation.

J Neural Eng 2017 04 13;14(2):025001. Epub 2017 Jan 13.

Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Objective: One goal of neuromorphic engineering is to create 'realistic' robotic systems that interact with the physical world by adopting neuromechanical principles from biology. Critical to this is the methodology to implement the spinal circuitry responsible for the behavior of afferented muscles. At its core, muscle afferentation is the closed-loop behavior arising from the interactions among populations of muscle spindle afferents, alpha and gamma motoneurons, and muscle fibers to enable useful behaviors.

Approach: We used programmable very- large-scale-circuit (VLSI) hardware to implement simple models of spiking neurons, skeletal muscles, muscle spindle proprioceptors, alpha-motoneuron recruitment, gamma motoneuron control of spindle sensitivity, and the monosynaptic circuitry connecting them. This multi-scale system of populations of spiking neurons emulated the physiological properties of a pair of antagonistic afferented mammalian muscles (each simulated by 1024 alpha- and gamma-motoneurones) acting on a joint via long tendons.

Main Results: This integrated system was able to maintain a joint angle, and reproduced stretch reflex responses even when driving the nonlinear biomechanics of an actual cadaveric finger. Moreover, this system allowed us to explore numerous values and combinations of gamma-static and gamma-dynamic gains when driving a robotic finger, some of which replicated some human pathological conditions. Lastly, we explored the behavioral consequences of adopting three alternative models of isometric muscle force production. We found that the dynamic responses to rate-coded spike trains produce force ramps that can be very sensitive to tendon elasticity, especially at high force output.

Significance: Our methodology produced, to our knowledge, the first example of an autonomous, multi-scale, neuromorphic, neuromechanical system capable of creating realistic reflex behavior in cadaveric fingers. This research platform allows us to explore the mechanisms behind healthy and pathological sensorimotor function in the physical world by building them from first principles, and it is a precursor to neuromorphic robotic systems.
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http://dx.doi.org/10.1088/1741-2552/aa593cDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540665PMC
April 2017

Unilateral Eccentric Contraction of the Plantarflexors Leads to Bilateral Alterations in Leg Dexterity.

Front Physiol 2016 30;7:582. Epub 2016 Nov 30.

Division of Biokinesiology and Physical Therapy, University of Southern California Los Angeles, CA, USA.

Eccentric contractions can affect musculotendon mechanical properties and disrupt muscle proprioception, but their behavioral consequences are poorly understood. We tested whether repeated eccentric contractions of plantarflexor muscles of one leg affected the dexterity of either leg. Twenty healthy male subjects (27.3 ± 4.0 yrs) compressed a compliant and slender spring prone to buckling with each isolated leg. The maximal instability they could control (i.e., the maximal average sustained compression force, or lower extremity dexterity force, LED) quantified the dexterity of each leg. We found that eccentric contractions did not affect LED, but reduced force variability (LED). Surprisingly, LED increased in the non-exposed, contralateral leg. These effects were specific to exposure to eccentric contractions because an effort-matched exposure to walking did not affect leg dexterity. In the exposed leg, eccentric contractions (i) reduced voluntary error corrections during spring compressions (i.e., reduced 0.5-4 Hz power of LED); (ii) did not change spinal excitability (i.e., unaffected H-reflexes); and (iii) changed the structure of the neural drive to the α-motoneuron pool (i.e., reduced EMG power within the 4-8 Hz physiological tremor band). These results suggest that repeated eccentric contractions alter the feedback control for dexterity in the exposed leg by reducing muscle spindle sensitivity. Moreover, the unexpected improvement in LED in the non-exposed contralateral leg was likely a consequence of crossed-effects on its spinal and supraspinal feedback control. We discuss the implications of these bilateral effects of unilateral eccentric contractions, their effect on spinal and supraspinal control of dynamic foot-ground interactions, and their potential to facilitate rehabilitation from musculoskeletal and neuromotor impairments.
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http://dx.doi.org/10.3389/fphys.2016.00582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127811PMC
November 2016

Robot-assisted and conventional therapies produce distinct rehabilitative trends in stroke survivors.

J Neuroeng Rehabil 2016 10 11;13(1):92. Epub 2016 Oct 11.

ETH Zurich and University of Zurich, Zurich, Switzerland.

Background: Comparing the efficacy of alternative therapeutic strategies for the rehabilitation of motor function in chronically impaired individuals is often inconclusive. For example, a recent randomized clinical trial (RCT) compared robot-assisted vs. conventional therapy in 77 patients who had had chronic motor impairment after a cerebrovascular accident. While patients assigned to robotic therapy had greater improvements in the primary outcome measure (change in score on the upper extremity section of the Fugl-Meyer assessment), the absolute difference between therapies was small, which left the clinical relevance in question.

Methods: Here we revisit that study to test whether the multidimensional rehabilitative response of these patients can better distinguish between treatment outcomes. We used principal components analysis to find the correlation of changes across seven outcome measures between the start and end of 8 weeks of therapy. Permutation tests verified the robustness of the principal components found.

Results: Each therapy in fact produces different rehabilitative trends of recovery across the clinical, functional, and quality of life domains. A rehabilitative trend is a principal component that quantifies the correlations among changes in outcomes with each therapy.

Conclusions: These findings challenge the traditional emphasis of RCTs on using a single primary outcome measure to compare rehabilitative responses that are naturally multidimensional. This alternative approach to, and interpretation of, the results of RCTs may will lead to more effective therapies targeted for the multidimensional mechanisms of recovery.

Trial Registration: ClinicalTrials.gov number NCT00719433 . Registered July 17, 2008.
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http://dx.doi.org/10.1186/s12984-016-0199-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5057463PMC
October 2016

The Dynamics of Voluntary Force Production in Afferented Muscle Influence Involuntary Tremor.

Front Comput Neurosci 2016 19;10:86. Epub 2016 Aug 19.

Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA; Division of Biokinesiology and Physical Therapy, University of Southern CaliforniaLos Angeles, CA, USA.

Voluntary control of force is always marked by some degree of error and unsteadiness. Both neural and mechanical factors contribute to these fluctuations, but how they interact to produce them is poorly understood. In this study, we identify and characterize a previously undescribed neuromechanical interaction where the dynamics of voluntary force production suffice to generate involuntary tremor. Specifically, participants were asked to produce isometric force with the index finger and use visual feedback to track a sinusoidal target spanning 5-9% of each individual's maximal voluntary force level. Force fluctuations and EMG activity over the flexor digitorum superficialis (FDS) muscle were recorded and their frequency content was analyzed as a function of target phase. Force variability in either the 1-5 or 6-15 Hz frequency ranges tended to be largest at the peaks and valleys of the target sinusoid. In those same periods, FDS EMG activity was synchronized with force fluctuations. We then constructed a physiologically-realistic computer simulation in which a muscle-tendon complex was set inside of a feedback-driven control loop. Surprisingly, the model sufficed to produce phase-dependent modulation of tremor similar to that observed in humans. Further, the gain of afferent feedback from muscle spindles was critical for appropriately amplifying and shaping this tremor. We suggest that the experimentally-induced tremor may represent the response of a viscoelastic muscle-tendon system to dynamic drive, and therefore does not fall into known categories of tremor generation, such as tremorogenic descending drive, stretch-reflex loop oscillations, motor unit behavior, or mechanical resonance. Our findings motivate future efforts to understand tremor from a perspective that considers neuromechanical coupling within the context of closed-loop control. The strategy of combining experimental recordings with physiologically-sound simulations will enable thorough exploration of neural and mechanical contributions to force control in health and disease.
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http://dx.doi.org/10.3389/fncom.2016.00086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990560PMC
September 2016
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