The equilibrium-point hypothesis--past, present and future

Development and verification of a physiologically motivated internal controller for the open-source extended Hill-type muscle model in LS-DYNA

Nowadays, active human body models are becoming essential tools for the development of integrated occupant safety systems. However, their broad application in industry and research is limited due to the complexity of incorporated muscle controllers, the long simulation runtime, and the non-regular use of physiological motor control approaches. The purpose of this study is to address the challenges in all indicated directions by implementing a muscle controller with several physiologically inspired control strategies into an open-source extended Hill-type muscle model formulated as LS-DYNA user-defined umat41 subroutine written in the Fortran programming language. This results in increased usability, runtime performance and physiological accuracy compared to the standard muscle material existing in LS-DYNA. The proposed controller code is verified with extensive experimental data that include findings for arm muscles, the cervical spine region, and the whole body. Selected verification experiments cover three different muscle activation situations: (1) passive state, (2) open-loop and closed-loop muscle activation, and (3) reflexive behaviour. Two whole body finite element models, the 50th percentile female VIVA OpenHBM and the 50th percentile male THUMS v5, are used for simulations, complemented by the simplified arm model extracted from the 50th percentile male THUMS v3. The obtained results are evaluated additionally with the CORrelation and Analysis methodology and the mean squared error method, showing good to excellent biofidelity and sufficient agreement with the experimental data. It was shown additionally how the integrated controller allows simplified mimicking of the movements for similar musculoskeletal models using the parameters transfer method. Furthermore, the Hill-type muscle model presented in this paper shows better kinematic behaviour even in the passive case compared to the existing one in LS-DYNA due to its improved damping and elastic properties. These findings provide a solid evidence base motivating the application of the enhanced muscle material with the internal controller in future studies with Active Human Body Models under different loading conditions.

Large-scale intramuscular electrode system for chronic electromyography and functional electrical stimulation

To understand how the central nervous system (CNS) enacts movements, it seems important to monitor the activities of the many muscles involved. Likewise, to restore complex movements to paralyzed limbs with electrical stimulation requires access to most limb muscles. Intramuscular electrodes are needed to obtain isolated recordings or stimulation of individual muscles. As such, we developed and tested the stability of large arrays of implanted intramuscular electrodes. We implanted 58 electrodes in 29 upper limb muscles in each of three macaques. Electrode connectors were protected within a skull-mounted chamber. During surgery, wires were tunneled subcutaneously to target muscles, where gold anchors were crimped onto the leads. The anchors were then deployed with an insertion device. In two monkeys, the chamber was fixed to the skull with a titanium baseplate rather than acrylic cement. In multiple sessions up to 15 wk after surgery, electromyographic (EMG) signals were recorded while monkeys made the same reaching movement. EMG signals were stable, with an average (SD) coefficient of variation across sessions of 0.24 ± 0.15. In addition, at 4, 8, and 16 wk after surgery, forces to incrementing stimulus pulses were measured for each electrode. The threshold current needed to evoke a response at 16 wk was not different from that at 4 wk. Likewise, peak force evoked by 16 mA of current at 16 wk was not different from 4 wk. The stability of this system implies it could be effectively used to monitor and stimulate large numbers of muscles needed to understand the control of natural and evoked movements.NEW AND NOTEWORTHY A new method was developed to enable long-lasting recording and stimulation of large numbers of muscles with intramuscular electrodes. Electromyographic signals and evoked force responses in 29 upper limb muscles remained stable over several months when tested in nonhuman primates. This system could be used effectively to monitor and stimulate numerous muscles needed to more fully understand the control of natural and evoked movements.

An informal logic of feedback-based temporal control

A conceptual framework and mathematical model of the control of articulatory timing are presented, in which feedback systems play a fundamental role. The model applies both to relatively small timescales, such as within syllables, and to relatively large timescales, such as multi-phrase utterances. A crucial distinction is drawn between internal/predictive feedback and external/sensory feedback. It is argued that speakers modulate attention to feedback to speed up and slow down speech. A number of theoretical implications of the framework are discussed, including consequences for the understanding of syllable structure and prosodic phrase organization.

Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point Control

Wearable robots are envisioned to amplify the independence of people with movement impairments by providing daily physical assistance. For portable, comfortable, and safe devices, soft pneumatic-based robots are emerging as a potential solution. However, due to the inherent complexities, including compliance and nonlinear mechanical behavior, feedback control for facilitating human–robot interaction remains a challenge. Herein, we present the design, fabrication, and control architecture of a soft wearable robot that assists in supination and pronation of the forearm. The soft wearable robot integrates an antagonistic pair of pneumatic-based helical actuators to provide active pronation and supination torques. Our main contribution is a bio-inspired equilibrium-point control scheme for integrating proprioceptive feedback and exteroceptive input (e.g., the user’s muscle activation signals) directly with the on/off valve behavior of the soft pneumatic actuators. The proposed human–robot controller is directly inspired by the equilibrium-point hypothesis of motor control, which suggests that voluntary movements arise through shifts in the equilibrium state of the antagonistic muscle pair spanning a joint. We hypothesized that the proposed method would reduce the required effort during dynamic manipulation without affecting the error. In order to evaluate our proposed method, we recruited seven pediatric participants with movement disorders to perform two dynamic interaction tasks with a haptic manipulandum. Each task required the participant to track a sinusoidal trajectory while the haptic manipulandum behaved as a Spring-Dominate system or Inertia-Dominate system. Our results reveal that the soft wearable robot, when active, reduced user effort on average by 14%. This work demonstrates the practical implementation of an equilibrium-point volitional controller for wearable robots and provides a foundational path toward versatile, low-cost, and soft wearable robots.

Human Control Model Estimation in Physical Human-Machine Interaction: A Survey

The study of human-machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human-machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

The computational neurology of movement under active inference

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.

Immunoceptive inference: why are psychiatric disorders and immune responses intertwined?

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind-body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems?

Generative Models for Active Vision

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference-which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach treats action as ensuring sensations conform to predictions (i.e., by moving the eyes) and posits that visual percepts are the consequence of updating predictions to conform to sensations. Under active inference, the challenge is to identify the form of the generative model that makes these predictions-and thus directs behavior. In this paper, we provide an overview of the generative models that the brain must employ to engage in active vision. This means specifying the processes that explain retinal cell activity and proprioceptive information from oculomotor muscle fibers. In addition to the mechanics of the eyes and retina, these processes include our choices about where to move our eyes. These decisions rest upon beliefs about salient locations, or the potential for information gain and belief-updating. A key theme of this paper is the relationship between "looking" and "seeing" under the brain's implicit generative model of the visual world.

Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy

Voluntary movements, like point-to-point or oscillatory human arm movements, are generated by the interaction of several structures. High-level neuronal circuits in the brain are responsible for planning and initiating a movement. Spinal circuits incorporate proprioceptive feedback to compensate for deviations from the desired movement. Muscle biochemistry and contraction dynamics generate movement driving forces and provide an immediate physical response to external forces, like a low-level decentralized controller. A simple central neuronal command like "initiate a movement" then recruits all these biological structures and processes leading to complex behavior, e.g., generate a stable oscillatory movement in resonance with an external spring-mass system. It has been discussed that the spinal feedback circuits, the biochemical processes, and the biomechanical muscle dynamics contribute to the movement generation, and, thus, take over some parts of the movement generation and stabilization which would otherwise have to be performed by the high-level controller. This contribution is termed morphological computation and can be quantified with information entropy-based approaches. However, it is unknown whether morphological computation actually differs between these different hierarchical levels of the control system. To investigate this, we simulated point-to-point and oscillatory human arm movements with a neuro-musculoskeletal model. We then quantify morphological computation on the different hierarchy levels. The results show that morphological computation is highest for the most central (highest) level of the modeled control hierarchy, where the movement initiation and timing are encoded. Furthermore, they show that the lowest neuronal control layer, the muscle stimulation input, exploits the morphological computation of the biochemical and biophysical muscle characteristics to generate smooth dynamic movements. This study provides evidence that the system's design in the mechanical as well as in the neurological structure can take over important contributions to control, which would otherwise need to be performed by the higher control levels.

The FACTS model of speech motor control: Fusing state estimation and task-based control

We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. FACTS employs a hierarchical state feedback control architecture to control simulated vocal tract and produce intelligible speech. The model includes higher-level control of speech tasks and lower-level control of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, after Task Dynamics. Both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This estimate is derived, based on efference copy of applied controls, from a forward model that predicts both the next vocal tract state as well as expected auditory and somatosensory feedback. A comparison between predicted feedback and actual feedback is then used to update the internal state prediction. FACTS is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

Modeling the Role of Sensory Feedback in Speech Motor Control and Learning

Purpose While the speech motor system is sensitive to feedback perturbations, sensory feedback does not seem to be critical to speech motor production. How the speech motor system is able to be so flexible in its use of sensory feedback remains an open question. Method We draw on evidence from a variety of disciplines to summarize current understanding of the sensory systems' role in speech motor control, including both online control and motor learning. We focus particularly on computational models of speech motor control that incorporate sensory feedback, as these models provide clear encapsulations of different theories of sensory systems' function in speech production. These computational models include the well-established directions into velocities of articulators model and computational models that we have been developing in our labs based on the domain-general theory of state feedback control (feedback aware control of tasks in speech model). Results After establishing the architecture of the models, we show that both the directions into velocities of articulators and state feedback control/feedback aware control of tasks models can replicate key behaviors related to sensory feedback in the speech motor system. Although the models agree on many points, the underlying architecture of the 2 models differs in a few key ways, leading to different predictions in certain areas. We cover key disagreements between the models to show the limits of our current understanding and point toward areas where future experimental studies can resolve these questions. Conclusions Understanding the role of sensory information in the speech motor system is critical to understanding speech motor production and sensorimotor learning in healthy speakers as well as in disordered populations. Computational models, with their concrete implementations and testable predictions, are an important tool to understand this process. Comparison of different models can highlight areas of agreement and disagreement in the field and point toward future experiments to resolve important outstanding questions about the speech motor control system.

Fascial organisation of motor synergies: a hypothesis

In the field of biomechanics and motor control understanding movement coordination is paramount. Motor synergies represent the coordination of neural and physical elements embedded in our bodies in order to optimize the solutions to motor problems. Although we are able to measure and quantify the movement made manifested, we do not have confidence in explaining the anatomical bases of its organisation at different levels. It is our contention that the flexible hierarchical organization of movement relies on the fascial structurers to create functional linkages at different levels, and this concept attunes with the neural control of synergies. At the base of movement organization there is a (somatic) equilibrium point that exists on the fascia where the neurologically- and mechanically-generated tensions dynamically balance out. This somatic equilibrium point is at the base of postural control, afferent flow of information to the nervous system about the state of the muscles, and of the coordinative pre-activation of muscular contraction sequences specific for a synergy. Implications are discussed and suggestions for research and clinical applications are made.

The computational pharmacology of oculomotion

Many physiological and pathological changes in brain function manifest in eye-movement control. As such, assessment of oculomotion is an invaluable part of a clinical examination and affords a non-invasive window on several key aspects of neuronal computation. While oculomotion is often used to detect deficits of the sort associated with vascular or neoplastic events; subtler (e.g. pharmacological) effects on neuronal processing also induce oculomotor changes. We have previously framed oculomotor control as part of active vision, namely, a process of inference comprising two distinct but related challenges. The first is inferring where to look, and the second is inferring how to implement the selected action. In this paper, we draw from recent theoretical work on the neuromodulatory control of active inference. This allows us to simulate the sort of changes we would expect in oculomotor behaviour, following pharmacological enhancement or suppression of key neuromodulators-in terms of deciding where to look and the ensuing trajectory of the eye movement itself. We focus upon the influence of cholinergic and GABAergic agents on the speed of saccades, and consider dopaminergic and noradrenergic effects on more complex, memory-guided, behaviour. In principle, a computational approach to understanding the relationship between pharmacology and oculomotor behaviour affords the opportunity to estimate the influence of a given pharmaceutical upon neuronal function, and to use this to optimise therapeutic interventions on an individual basis.

Attainment of Quiet Standing in Humans: Are the Lower Limb Joints Controlled Relative to a Misaligned Postural Reference?

In human quiet standing, the relative position between ankle joint centre and line of gravity is neurally regulated within tight limits. The regulation of the knee and hip configuration is unclear and thought to be controlled passively. However, perturbed standing experiments have shown a lower limb multi-joint coordination. Here, measuring the relative alignment between lower limb joints and the line of gravity in quiet standing after walking, we investigated whether the configuration is maintained over time through passive mechanisms or active control. Thirteen healthy adults walked without following a path and then stood quietly for 7.6 s on a force platform (up to four trials). The transition between initiation and steady-state standing (7.6 s) was measured using motion capture. Sagittal lower limb joint centres' position relative to line of gravity (CoG_AP) and their time constants were calculated in each trial. Ankle, knee, and hip joint moments were also calculated through inverse dynamics. After walking, the body decelerated (τ = 0.16 s). The ankle and hip joints' position relative to CoG_AP measured at two time intervals of quiet standing (Mid = 0.5-0.55 s; End = 7.55-7.6 s) were different (mean ± SEM, CoG_{AP-Ankle_Mid} = 47 ± 4 mm, CoG_{AP-Ankle_End} = 58 ± 5 mm; CoG_{AP-Hip_Mid} = 2 ± 5 mm, CoG_{AP-Hip_End} = -5 ± 5 mm). The ankle, knee, and hip flexion-extension moments significantly changed. Changes in joints position relative to CoG_AP and misalignment suggest that joint position is not maintained over 7.6 s, but regulated relative to a standing reference. Higher joint moments at steady-state standing suggest mechanisms other than passive knee and hip regulation are involved in standing.

Important Movement Concepts: Clinical Versus Neuroscience Perspectives

Human movement is complex, presenting clinical and research challenges regarding how it is described and investigated. This paper discusses the commonalities and differences on how human movement is conceptualized from neuroscientific and clinical perspectives with respect to postural control; the limitations of linear measures; movement efficiency with respect to metabolic energy cost and selectivity; and, how muscle synergy analysis may contribute to our understanding of movement variability. We highlight the role of sensory information on motor performance with respect to the base of support and alignment, illustrating a potential disconnect between the clinical and neuroscientific perspectives. The purpose of this paper is to discuss the commonalities and differences in how movement concepts are defined and operationalized by Bobath clinicians and the neuroscientific community to facilitate a common understanding and open the dialogue on the research practice gap.

Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates

For years, neurophysiological studies of the cerebral cortical mechanisms of voluntary motor control were limited to single-electrode recordings of the activity of one or a few neurons at a time. This approach was supported by the widely accepted belief that single neurons were the fundamental computational units of the brain (the “neuron doctrine”). Experiments were guided by motor-control models that proposed that the motor system attempted to plan and control specific parameters of a desired action, such as the direction, speed or causal forces of a reaching movement in specific coordinate frameworks, and that assumed that the controlled parameters would be expressed in the task-related activity of single neurons. The advent of chronically implanted multi-electrode arrays about 20 years ago permitted the simultaneous recording of the activity of many neurons. This greatly enhanced the ability to study neural control mechanisms at the population level. It has also shifted the focus of the analysis of neural activity from quantifying single-neuron correlates with different movement parameters to probing the structure of multi-neuron activity patterns to identify the emergent computational properties of cortical neural circuits. In particular, recent advances in “dimension reduction” algorithms have attempted to identify specific covariance patterns in multi-neuron activity which are presumed to reflect the underlying computational processes by which neural circuits convert the intention to perform a particular movement into the required causal descending motor commands. These analyses have led to many new perspectives and insights on how cortical motor circuits covertly plan and prepare to initiate a movement without causing muscle contractions, transition from preparation to overt execution of the desired movement, generate muscle-centered motor output commands, and learn new motor skills. Progress is also being made to import optical-imaging and optogenetic toolboxes from rodents to non-human primates to overcome some technical limitations of multi-electrode recording technology.

A computational scheme for internal models not requiring precise system parameters

Utilization by humans of a precise and adaptable internal model of the dynamics of the body in generating movements is a well-supported concept. The prevailing opinion is that such an internal model ceaselessly develops through long-term repetition and accumulation in the central nervous system (CNS). However, a long-term learning process would not be absolutely necessary for the formation of internal models. It is possible to estimate the dynamics of the system by using a motor command and its resulting output, instead of constructing a model of the dynamics with precise parameters. In this study, a computational model is proposed that uses a motor command and its corresponding output to estimate the dynamics of the system and it is examined whether the proposed model is capable of describing a series of empirical movements. The proposed model was found to be capable of describing humans' fast movements which require compensation for system dynamics as well as sensory delays. In addition, the proposed model shows equifinality under inertial perturbations as seen in several experimental studies. This satisfactory reproducibility of the proposed computation raises the possibility that humans make a movement by estimating the system dynamics with a copy of motor command and sensory output on a momentary basis, without the need to identify precise system parameters.

Gravitational torque partially accounts for proprioceptive acuity

Proprioception of the upper extremity is commonly measured using joint position sense tasks. Recent evidence suggests heightened position sense at elevation angles in the shoulder and elbow near 90° in the sagittal plane. The influence of external torque has been suggested to play a pivotal role in the heightened acuity in elevated positions due to increased moment arm with respect to gravitational vectors. We hypothesized that the addition of a buoyance vector in opposition to this gravitational vector would reduce the influence of torque on proprioceptive acuity, resulting in consistent position sense errors with respect to elevation angle. Joint position sense was measured using an apple iPod touch using a custom application. Participants elevated their arm to 50, 70 and 90° of elevation in the sagittal plane in the absence of visual feedback. Data were collected in three conditions, normal (control) and submerged and weighted. We found angular differences between control and submerged conditions, but not between control and weighted conditions. When the arm was elevated to 90° in the submerged condition, we found participants undershot the target position by approximately -0.5° with the addition of the buoyancy force vector. Participants without this buoyancy vector at the same target position consistently overshot the target by approximately 2.0°, which suggests that external torque may be more involved in the direction of proprioceptive errors more than the magnitude of the error as the magnitude of the difference was relatively small (2.5°).

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

Background: The efficacy of robot-assisted rehabilitation as a technique for achieving motor recovery is still being debated. The effects of robotic assistance are generally measured using standard clinical assessments. Few studies have investigated the value of human-centered instrumental analysis, taking the modular organization of the human neuromotor system into account in assessing how stroke survivors interact with robotic set-ups. In this paper, muscle synergy analysis was coupled with clustering procedures to elucidate the effect of human-robot interaction on the spatial and temporal features, and directional tuning of motor modules during robot-assisted movements. Methods: Twenty-two stroke survivors completed a session comprising a series of hand-to-mouth movements with and without robotic assistance. Patients were assessed instrumentally, recording kinematic, and electromyographic data to extract spatial muscle synergies and their temporal components. Patients' spatial synergies were grouped by means of a cluster analysis, matched pairwise across conditions (free and robot-assisted movement), and compared in terms of their spatial and temporal features, and directional tuning, to examine how robotic assistance altered their motor modules. Results: Motor synergies were successfully extracted for all 22 patients in both conditions. Seven clusters (spatial synergies) could describe the original datasets, in both free and robot-assisted movements. Interacting with the robot slightly altered the spatial synergies' features (to a variable extent), as well as their temporal components and directional tuning. Conclusions: Slight differences were identified in the characteristics of spatial synergies, temporal components and directional tuning of the motor modules of stroke survivors engaging in free and robot-assisted movements. Such effects are worth investigating in the framework of a modular description of the neuromusculoskeletal system to shed more light on human-robot interaction, and the effects of robotic assistance and rehabilitation.

Improved Learning a Coincident Timing Task With a Predictable Resisting Force

The present study analyzes the learning in a coincident timing task with force perturbation. We aimed to verify whether a predictable load (constant spring) applied to hand movements could facilitate learning and, thus, performance improvement with respect to movements without any external load and an unpredictable load to perform a coincident timing task with a few number of repetitions (n = 28) under acquisition and transfer phases. The results showed that the group with a predictable load had a significant better performance with lower percentage of errors and smaller time variance in the acquisition and transfer phase. The groups with no load and unpredictable load had a similar performance in the transfer phase. It can be concluded that adding a predictable force to the coincident timing task results in performance improvement. Therefore, learning to reach a target at a correct time could be improved with the application of predictable external loads.

Efficacy of a knee orthosis that uses an elastic element

In this study, we evaluate the support effect of a knee orthosis that uses the elasticity element from the perspective of human motor control. The speeds during level-ground walking and the angles during slope walking were varied during the experiments. It was observed that the support effect was remarkable at 4 km/h during the level-ground walking. In particular, at 12° during slope walking, the strength of the stretching muscle decreased for the knee joint in the stance phase and the hip joint in the swing phase. The results show that this orthosis exhibits a different effect from the conventional type adjustment to damping in the swing phase.

Estimation of Time-Varying, Intrinsic and Reflex Dynamic Joint Stiffness during Movement. Application to the Ankle Joint

Dynamic joint stiffness determines the relation between joint position and torque, and plays a vital role in the control of posture and movement. Dynamic joint stiffness can be quantified during quasi-stationary conditions using disturbance experiments, where small position perturbations are applied to the joint and the torque response is recorded. Dynamic joint stiffness is composed of intrinsic and reflex mechanisms that act and change together, so that nonlinear, mathematical models and specialized system identification techniques are necessary to estimate their relative contributions to overall joint stiffness. Quasi-stationary experiments have demonstrated that dynamic joint stiffness is heavily modulated by joint position and voluntary torque. Consequently, during movement, when joint position and torque change rapidly, dynamic joint stiffness will be Time-Varying (TV). This paper introduces a new method to quantify the TV intrinsic and reflex components of dynamic joint stiffness during movement. The algorithm combines ensemble and deterministic approaches for estimation of TV systems; and uses a TV, parallel-cascade, nonlinear system identification technique to separate overall dynamic joint stiffness into intrinsic and reflex components from position and torque records. Simulation studies of a stiffness model, whose parameters varied with time as is expected during walking, demonstrated that the new algorithm accurately tracked the changes in dynamic joint stiffness using as little as 40 gait cycles. The method was also used to estimate the intrinsic and reflex dynamic ankle stiffness from an experiment with a healthy subject during which ankle movements were imposed while the subject maintained a constant muscle contraction. The method identified TV stiffness model parameters that predicted the measured torque very well, accounting for more than 95% of its variance. Moreover, both intrinsic and reflex dynamic stiffness were heavily modulated through the movement in a manner that could not be predicted from quasi-stationary experiments. The new method provides the tool needed to explore the role of dynamic stiffness in the control of movement.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

Should Ballet Dancers Vary Postures and Underfoot Surfaces When Practicing Postural Balance?

BACKGROUND

Postural balance (PB) is an important component skill for professional dancers. However, the effects of different types of postures and different underfoot surfaces on PB have not adequately been addressed.

PURPOSE

The main aim of this study was to investigate the effect of different conditions of footwear, surfaces, and standing positions on static and dynamic PB ability of young ballet dancers.

METHODS

A total of 36 male and female young professional ballet dancers (aged 14-19 years) completed static and dynamic balance testing, measured by head and lumbar accelerometers, while standing on one leg in the turnout position, under six different conditions: (1) "relaxed" posture; (2) "ballet" posture; (3) barefoot; (4) ballet shoes with textured insoles; (5) barefoot on a textured mat; and (6) barefoot on a spiky mat.

RESULTS

A condition effect was found for static and dynamic PB. Static PB was reduced when dancers stood in the ballet posture compared with standing in the relaxed posture and when standing on a textured mat and on a spiky mat (p < .05), and static PB in the relaxed posture was significantly better than PB in all the other five conditions tested. Dynamic PB was significantly better while standing in ballet shoes with textured insoles and when standing on a spiky mat compared with all other conditions (p < .05).

CONCLUSIONS

The practical implications derived from this study are that both male and female dancers should try to be relaxed in their postural muscles when practicing a ballet aligned position, including dance practice on different types of floors and on different types of textured/spiky materials may result in skill transfer to practice on normal floor surfaces, and both static and dynamic PB exercises should be assessed and generalized into practical dance routines.

Cortical circuits and modules in movement generation: experiments and theories

Here we review recent studies of the cortical circuits subserving the control of posture and movement. This topic is addressed from neurophysiological and evolutionary perspectives describing recent advancements achieved through experimental studies in humans and non-human primates. We also describe current debates and controversies concerning motor mapping within the motor cortex and the different computational approaches aimed at resolving the mystery around motor representations and computations. In recent years there is growing interest in the possibly modular organization of motor representations and dynamical processes and the potential of such studies to provide new clues into motor information processing. Hence this review focuses on motor modularity, highlighting the new research directions inspired by empirical findings and theoretical models developed within the last several years.

Active inference and robot control: a case study

Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours.

Human-like object tracking and gaze estimation with PKD android

As the use of robots increases for tasks that require human-robot interactions, it is vital that robots exhibit and understand human-like cues for effective communication. In this paper, we describe the implementation of object tracking capability on Philip K. Dick (PKD) android and a gaze tracking algorithm, both of which further robot capabilities with regard to human communication. PKD's ability to track objects with human-like head postures is achieved with visual feedback from a Kinect system and an eye camera. The goal of object tracking with human-like gestures is twofold : to facilitate better human-robot interactions and to enable PKD as a human gaze emulator for future studies. The gaze tracking system employs a mobile eye tracking system (ETG; SensoMotoric Instruments) and a motion capture system (Cortex; Motion Analysis Corp.) for tracking the head orientations. Objects to be tracked are displayed by a virtual reality system, the Computer Assisted Rehabilitation Environment (CAREN; MotekForce Link). The gaze tracking algorithm converts eye tracking data and head orientations to gaze information facilitating two objectives: to evaluate the performance of the object tracking system for PKD and to use the gaze information to predict the intentions of the user, enabling the robot to understand physical cues by humans.

The Averaged EMGs Recorded from the Arm Muscles During Bimanual "Rowing" Movements

The main purpose was to analyze quantitatively the the average surface EMGs of the muscles that function around the elbow and shoulder joints of both arms in bimanual “rowing” movements, which were produced under identical elastic loads applied to the levers (“oars”). The muscles of PM group (“pulling” muscles: elbow flexors, shoulder extensors) generated noticeable velocity-dependent dynamic EMG components during the pulling and returning phases of movement and supported a steady-state activity during the hold phase. The muscles of RM group (“returning” muscles: elbow extensors, shoulder flexors) co-contracted with PM group during the movement phases and decreased activity during the hold phase. The dynamic components of the EMGs strongly depended on the velocity factor in both muscle groups, whereas the side and load factors and combinations of various factors acted only in PM group. Various subjects demonstrated diverse patterns of activity redistribution among muscles. We assume that central commands to the same muscles in two arms may be essentially different during execution of similar movement programs. Extent of the diversity in the EMG patterns of such muscles may reflect the subject's skilling in motor performance; on the other hand, the diversity can be connected with redistribution of activity between synergic muscles, thus providing a mechanism directed against development of the muscle fatigue.

Techniques and Methods for Testing the Postural Function in Healthy and Pathological Subjects

The different techniques and methods employed as well as the different quantitative and qualitative variables measured in order to objectify postural control are often chosen without taking into account the population studied, the objective of the postural test, and the environmental conditions. For these reasons, the aim of this review was to present and justify the different testing techniques and methods with their different quantitative and qualitative variables to make it possible to precisely evaluate each sensory, central, and motor component of the postural function according to the experiment protocol under consideration. The main practical and technological methods and techniques used in evaluating postural control were explained and justified according to the experimental protocol defined. The main postural conditions (postural stance, visual condition, balance condition, and test duration) were also analyzed. Moreover, the mechanistic exploration of the postural function often requires implementing disturbing postural conditions by using motor disturbance (mechanical disturbance), sensory stimulation (sensory manipulation), and/or cognitive disturbance (cognitive task associated with maintaining postural balance) protocols. Each type of disturbance was tackled in order to facilitate understanding of subtle postural control mechanisms and the means to explore them.

How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.

An adaptive spinal-like controller: tunable biomimetic behavior for a robotic limb

Background

Spinal-like regulators have recently been shown to support complex behavioral patterns during volitional goal-oriented reaching paradigms. We use an interpretation of the adaptive spinal-like controller as inspiration for the development of a controller for a robotic limb. It will be demonstrated that a simulated robot arm with linear actuators can achieve biological-like limb movements. In addition, it will be shown that programmability in the regulator enables independent spatial and temporal changes to be defined for movement tasks, downstream of central commands using sensory stimuli. The adaptive spinal-like controller is the first to demonstrate such behavior for complex motor behaviors in multi-joint limb movements.

Methods

The controller is evaluated using a simulated robotic apparatus and three goal-oriented reaching paradigms: 1) shaping of trajectory profiles during reaching; 2) sensitivity of trajectories to sudden perturbations; 3) reaching to a moving target. The experiments were designed to highlight complex motor tasks that are omitted in earlier studies, and important for the development of improved artificial limb control.

Results

In all three cases the controller was able to reach the targets without a priori planning of end-point or segmental motor trajectories. Instead, trajectory spatio-temporal dynamics evolve from properties of the controller architecture using the spatial error (vector distance to goal). Results show that curvature amplitude in hand trajectory paths are reduced by as much as 98% using simple gain scaling techniques, while adaptive network behavior allows the regulator to successfully adapt to perturbations and track a moving target. An important observation for this study is that all motions resemble human-like movements with non-linear muscles and complex joint mechanics.

Conclusions

The controller shows that it can adapt to various behavioral contexts which are not included in previous biomimetic studies. The research supplements an earlier study by examining the tunability of the spinal-like controller for complex reaching tasks. This work is a step toward building more robust controllers for powered artificial limbs.

From spontaneous motor activity to coordinated behaviour: a developmental model

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.

A Simplified Spinal-Like Controller Facilitates Muscle Synergies and Robust Reaching Motions

We develop an adaptive controller for multi-joint, multi-muscle arm movements based on simplified spinal-like circuits found in the periphery, muscle synergies, and interpretations of gain-field projections from reach related neurons in the Superior Colliculus. The resulting innovation provides a highly robust sensory based controller that can be adapted to systems which require multi-muscle co-ordination. It provides human-like responses during perturbations elicited either internally or by the environment and for simple point-to-point reaching. We simulate limb motion and EMGs in Simulink using Virtual Muscle models and a variety of paradigms, including motion with external perturbations, and varying levels of antagonist muscle co-contractions. The results show that the system can exhibit smooth coordinated motions, without explicit kinematic or dynamic planning even in the presence of perturbations. In addition, we show by varying the level of muscle co-contractions from 0% to 40%, that the effects of external perturbations on joint trajectories can be reduced by up to 42%. The improved controller design is novel providing robust behavior during dynamic events and an automatic adaptive response from sensory-integration.

A model of motor performance during surface penetration: from physics to voluntary control

The act of puncturing a surface with a hand-held tool is a ubiquitous but complex motor behavior that requires precise force control to avoid potentially severe consequences. We present a detailed model of puncture over a time course of approximately 1,000 ms, which is fit to kinematic data from individual punctures, obtained via a simulation with high-fidelity force feedback. The model describes puncture as proceeding from purely physically determined interactions between the surface and tool, through decline of force due to biomechanical viscosity, to cortically mediated voluntary control. When fit to the data, it yields parameters for the inertial mass of the tool/person coupling, time characteristic of force decline, onset of active braking, stopping time and distance, and late oscillatory behavior, all of which the analysis relates to physical variables manipulated in the simulation. While the present data characterize distinct phases of motor performance in a group of healthy young adults, the approach could potentially be extended to quantify the performance of individuals from other populations, e.g., with sensory-motor impairments. Applications to surgical force control devices are also considered.

Outsider interference: no role for motor lateralization in determining the strength of avoidance responses during reaching

When reaches are performed toward target objects, the presence of other non-target objects influences kinematic parameters of the reach. A typical observation has been that non-targets positioned ipsilaterally to the acting limb interfere more with the trajectory of the hand than contralateral non-targets. Here, we investigate whether this effect is mediated by motor lateralization or by the relative positioning of the objects with reference to the acting limb. Participants were asked to perform reaches toward physical target objects with their preferred or non-preferred hands while physical non-targets were present in different possible positions in the workspace. We tested both left-handers and right-handers. Our results show that a participant's handedness does not influence reaching behavior in an obstacle avoidance paradigm. Furthermore, no statistically significant differences between the use of the preferred and non-preferred hand were observed on the kinematic parameters of the reaches. We found evidence that non-targets positioned on the outside of the reaching limb influenced the reaching behavior more strongly than non-targets on the inside. Moreover, the type of movement also appeared to play a role, as reaches that crossed the workspace had a stronger effect on avoidance behavior than reaches that were 'uncrossed.' We interpret these results as support for the hypothesis that the avoidance response is determined by keeping a preferred distance between the acting limb in all stages of its reach toward the target and the non-target position. This process is not biased by hand dominance or the hand preference of the actor.

Humans make near-optimal adjustments of control to initial body configuration in vertical squat jumping

We investigated adjustments of control to initial posture in squat jumping. Eleven male subjects jumped from three initial postures: preferred initial posture (PP), a posture in which the trunk was rotated 18° more backward (BP) and a posture in which it was rotated 15° more forward (FP) than in PP. Kinematics, ground reaction forces and electromyograms (EMG) were collected. EMG was rectified and smoothed to obtain smoothed rectified EMG (srEMG). Subjects showed adjustments in srEMG histories, most conspicuously a shift in srEMG-onset of rectus femoris (REC): from early in BP to late in FP. Jumps from the subjects' initial postures were simulated with a musculoskeletal model comprising four segments and six Hill-type muscles, which had muscle stimulation (STIM) over time as input. STIM of each muscle changed from initial to maximal at STIM-onset, and STIM-onsets were optimized using jump height as criterion. Optimal simulated jumps from BP, PP and FP were similar to jumps of the subjects. Optimal solutions primarily differed in STIM-onset of REC: from early in BP to late in FP. Because the subjects' adjustments in srEMG-onsets were similar to adjustments of the model's optimal STIM-onsets, it was concluded that the former were near-optimal. With the model we also showed that near-maximum jumps from BP, PP and FP could be achieved when STIM-onset of REC depended on initial hip joint angle and STIM-onsets of the other muscles were posture-independent. A control theory that relies on a mapping from initial posture to STIM-onsets seems a parsimonious alternative to theories relying on internal optimal control models.

Predictions not commands: active inference in the motor system

The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular.