Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton.

Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different "transparent" controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation.

Reoptimization of single-joint motor patterns to non-Earth gravity torques induced by a robotic exoskeleton.

Gravity is a ubiquitous component of our environment that we have learned to optimally integrate in movement control. Yet, altered gravity conditions arise in numerous applications from space exploration to rehabilitation, thereby pressing the sensorimotor system to adapt. Here, we used a robotic exoskeleton to reproduce the elbow joint-level effects of arbitrary gravity fields ranging from 1g to -1g, passing through Mars- and Moon-like gravities, and tested whether humans can reoptimize their motor patterns accordingly. By comparing the motor patterns of actual arm movements with those predicted by an optimal control model, we show that our participants (N=61) adapted optimally to each gravity-like torque. These findings suggest that the joint-level effects of a large range of gravities can be efficiently apprehended by humans, thus opening new perspectives in arm weight support training in manipulation tasks, whether it be for patients or astronauts.

The value of time in the invigoration of human movements when interacting with a robotic exoskeleton.

Time and effort are thought to be subjectively balanced during the planning of goal-directed actions, thereby setting the vigor of volitional movements. Theoretical models predicted that the value of time should then amount to high levels of effort. However, the time-effort trade-off has so far only been studied for a narrow range of efforts. To investigate the extent to which humans can invest in a time-saving effort, we used a robotic exoskeleton to substantially vary the energetic cost associated with a certain vigor during reaching movements. In this situation, minimizing the time-effort trade-off should lead to high and low human efforts for upward and downward movements, respectively. Consistently, all participants expended substantial amounts of energy upward and remained essentially inactive by harnessing the work of gravity downward, while saving time in both cases. A common time-effort trade-off may therefore determine the vigor of reaching movements for a wide range of efforts.

Generating variability from motor primitives during infant locomotor development.

Motor variability is a fundamental feature of developing systems allowing motor exploration and learning. In human infants, leg movements involve a small number of basic coordination patterns called locomotor primitives, but whether and when motor variability could emerge from these primitives remains unknown. Here we longitudinally followed 18 infants on 2-3 time points between birth (~4 days old) and walking onset (~14 months old) and recorded the activity of their leg muscles during locomotor or rhythmic movements. Using unsupervised machine learning, we show that the structure of trial-to-trial variability changes during early development. In the neonatal period, infants own a minimal number of motor primitives but generate a maximal motor variability across trials thanks to variable activations of these primitives. A few months later, toddlers generate significantly less variability despite the existence of more primitives due to more regularity within their activation. These results suggest that human neonates initiate motor exploration as soon as birth by variably activating a few basic locomotor primitives that later fraction and become more consistently activated by the motor system.

Human babies start to walk on their own when they are about one year old, but before that, they can move their legs to produce movements called 'stepping', where they take steps when held over a surface; and kicking, where they kick in the air when lying on their backs. These two behaviors are known as 'locomotor precursors' and can be observed from birth. Previous studies suggest that infants produce these movements by activating a small number of motor primitives, different modules in the nervous system ­ each activating a combination of muscles to produce a movement. However, babies and toddlers exhibit a lot of variability when they move, which is a hallmark of typical development that furthers exploring and learning. So far, it has been unclear whether such differences arise as soon as babies are born and if so, how a small number of motor primitives could result in this variability. Hinnekens et al. hypothesized that the great variety of movements in infants can be generated from a small set of motor primitives, when several cycles of flexing and extending the legs are considered. To test their hypothesis, the researchers first needed to establish how and when infants generate this variability of movement. To do so, they used electromyography to record the leg muscle activity of 18 babies during either movement resulting in a body displacement (locomotor movement) or rhythmic movement. These measurements were taken at either two or three timepoints between birth and the onset of walking. Next, the scientists used a state-of-the-art machine learning approach to model the neural basis underlying these recordings, which showed that newborns generate a lot of movement variability, but they do so by activating a small number of motor primitives, which they can combine in different ways. Hinnekens et al. also show that as babies get older, the number of motor primitives increases while the variety of movements decreases due to a more steady activation of each motor primitive. Cerebral plasticity is maximal during the first year of life, and infants can regularly learn new motor skills, each leading to the ability to perform more movements. Motor variability is believed to play an important role in this learning process and is known to be decreased in atypical development. As such, examining motor variability may be a promising tool to identify neurodevelopmental delays at younger ages.

From Motivation to Action: Action Cost Better Predicts Changes in Premovement Beta-Band Activity than Speed.

Although premovement beta-band event-related desynchronization (ß-ERD; 13-30 Hz) from sensorimotor regions is modulated by movement speed, current evidence does not support a strict monotonic association between the two. Given that ß-ERD is thought to increase information encoding capacity, we tested the hypothesis that it might be related to the expected neurocomputational cost of movement, here referred to as action cost. Critically, action cost is greater both for slow and fast movements compared with a medium or "preferred" speed. Thirty-one right-handed participants performed a speed-controlled reaching task while recording their EEG. Results revealed potent modulations of beta power as a function of speed, with ß-ERD being significantly greater both for movements performed at high and low speeds compared with medium speed. Interestingly, medium-speed movements were more often chosen by participants than low-speed and high-speed movements, suggesting that they were evaluated as less costly. In line with this, modeling of action cost revealed a pattern of modulation across speed conditions that strikingly resembled the one found for ß-ERD. Indeed, linear mixed models showed that estimated action cost predicted variations of ß-ERD significantly better than speed. This relationship with action cost was specific to beta power, as it was not found when averaging activity in the mu band (8-12 Hz) and gamma band (31-49 Hz) bands. These results demonstrate that increasing ß-ERD may not merely speed up movements, but instead facilitate the preparation of high-speed and low-speed movements through the allocation of additional neural resources, thereby enabling flexible motor control.SIGNIFICANCE STATEMENT Heightened beta activity has been associated with movement slowing in Parkinson's disease, and modulations of beta activity are commonly used to decode movement parameters in brain-computer interfaces. Here we show that premovement beta activity is better explained by the neurocomputational cost of the action rather than its speed. Instead of being interpreted as a mere reflection of changes in movement speed, premovement changes in beta activity might therefore be used to infer the amount of neural resources that are allocated for motor planning.

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces.

Exoskeletons are among the most promising devices dedicated to assisting human movement during reeducation protocols and preventing musculoskeletal disorders at work. However, their potential is currently limited, partially because of a fundamental contradiction impacting their design. Indeed, increasing the interaction quality often requires the inclusion of passive degrees of freedom in the design of human-exoskeleton interfaces, which increases the exoskeleton's inertia and complexity. Thus, its control also becomes more complex, and unwanted interaction efforts can become important. In the present paper, we investigate the influence of two passive rotations in the forearm interface on sagittal plane reaching movements while keeping the arm interface unchanged (i.e., without passive degrees of freedom). Such a proposal represents a possible compromise between conflicting design constraints. The in-depth investigations carried out here in terms of interaction efforts, kinematics, electromyographic signals, and subjective feedback of participants all underscored the benefits of such a design. Therefore, the proposed compromise appears to be suitable for rehabilitation sessions, specific tasks at work, and future investigations into human movement using exoskeletons.

On the perception of movement vigour.

It is common to get the impression that someone moves rather slowly or quickly in everyday life. In motor control, the natural pace of movement is captured by the concept of vigour, which is often quantified from the speed or duration of goal-directed actions. A common phenomenon, here referred to as the vigour law, is that preferred speed and duration idiosyncratically increase with the magnitude of the motion. According to the direct-matching hypothesis, this vigour law could thus underlie the judgement of someone else's movement vigour. We conducted a series of three experiments (N = 80) to test whether the vigour law also exists in perception and whether it is linked to that of action. In addition to measuring participants' vigour, we also asked them to judge the quickness of stimuli representing horizontal arm reaching movements varying through amplitudes, speeds, and durations. Results showed that speed and duration of movements perceived as neither fast nor slow (i.e., natural pace) increased with amplitude, thereby indicating that the vigour law holds when an observer judges the natural pace of others' movements. Results also revealed that this judgement was population-based (related to the average vigour of all participants) rather than individual-based (participant's own vigour).

Optimization of modularity during development to simplify walking control across multiple steps.

Introduction: Walking in adults relies on a small number of modules, reducing the number of degrees of freedom that needs to be regulated by the central nervous system (CNS). While walking in toddlers seems to also involve a small number of modules when considering averaged or single-step data, toddlers produce a high amount of variability across strides, and the extent to which this variability interacts with modularity remains unclear. Methods: Electromyographic activity from 10 bilateral lower limb muscles was recorded in both adults (n = 12) and toddlers (n = 12) over 8 gait cycles. Toddlers were recorded while walking independently and while being supported by an adult. This condition was implemented to assess if motor variability persisted with reduced balance constraints, suggesting a potential central origin rather than reliance on peripheral regulations. We used non-negative matrix factorization to model the underlying modular command with the Space-by-Time Decomposition method, with or without averaging data, and compared the modular organization of toddlers and adults during multiple walking strides. Results: Toddlers were more variable in both conditions (i.e. independent walking and supported by an adult) and required significantly more modules to account for their greater stride-by-stride variability. Activations of these modules varied more across strides and were less parsimonious compared to adults, even with diminished balance constraints. Discussion: The findings suggest that modular control of locomotion evolves between toddlerhood and adulthood as the organism develops and practices. Adults seem to be able to generate several strides of walking with less modules than toddlers. The persistence of variability in toddlers when balance constraints were lowered suggests a link with the ability to explore rather than with corrective mechanisms. In conclusion, the capacity of new walkers to flexibly activate their motor command suggests a broader range of possible actions, though distinguishing between modular and non-modular inputs remains challenging.

Evidence for a cost of time in the invigoration of isometric reaching movements.

How the brain determines the vigor of goal-directed movements is a fundamental question in neuroscience. Recent evidence has suggested that vigor results from a trade-off between a cost related to movement production (cost of movement) and a cost related to our brain's tendency to temporally discount the value of future reward (cost of time). However, whether it is critical to hypothesize a cost of time to explain the vigor of basic reaching movements with intangible reward is unclear because the cost of movement may be theoretically sufficient for this purpose. Here we directly address this issue by designing an isometric reaching task whose completion can be accurate and effortless in prefixed durations. The cost of time hypothesis predicts that participants should be prone to spend energy to save time even if the task can be accomplished at virtually no motor cost. Accordingly, we found that all participants generated substantial amounts of force to invigorate task accomplishment, especially when the prefixed duration was long enough. Remarkably, the time saved by each participant was linked to their original vigor in the task and predicted by an optimal control model balancing out movement and time costs. Taken together, these results support the existence of an idiosyncratic, cognitive cost of time that underlies the invigoration of basic isometric reaching movements.NEW & NOTEWORTHY Movement vigor is generally thought to result from a trade-off between time and motor costs. However, it remains unclear whether the time cost only modulates vigor around some nominal value explained by a minimal motor cost or whether it determines movement invigoration more broadly. Here, we present an original paradigm allowing us to neutralize the cost of movement and provide new evidence that a cost of time must underlie the invigoration of isometric reaching movements.

Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach.

Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.

Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language.

Sign language (SL) motion contains information about the identity of a signer, as does voice for a speaker or gait for a walker. However, how such information is encoded in the movements of a person remains unclear. In the present study, a machine learning model was trained to extract the motion features allowing for the automatic identification of signers. A motion capture (mocap) system recorded six signers during the spontaneous production of French Sign Language (LSF) discourses. A principal component analysis (PCA) was applied to time-averaged statistics of the mocap data. A linear classifier then managed to identify the signers from a reduced set of principal components (PCs). The performance of the model was not affected when information about the size and shape of the signers were normalized. Posture normalization decreased the performance of the model, which nevertheless remained over five times superior to chance level. These findings demonstrate that the identity of a signer can be characterized by specific statistics of kinematic features, beyond information related to size, shape, and posture. This is a first step toward determining the motion descriptors necessary to account for the human ability to identify signers.

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.

Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.

A cross-species neural integration of gravity for motor optimization.

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan's neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.

Human Weight Compensation With a Backdrivable Upper-Limb Exoskeleton: Identification and Control.

Active exoskeletons are promising devices for improving rehabilitation procedures in patients and preventing musculoskeletal disorders in workers. In particular, exoskeletons implementing human limb's weight support are interesting to restore some mobility in patients with muscle weakness and help in occupational load carrying tasks. The present study aims at improving weight support of the upper limb by providing a weight model considering joint misalignments and a control law including feedforward terms learned from a prior population-based analysis. Three experiments, for design and validation purposes, are conducted on a total of 65 participants who performed posture maintenance and elbow flexion/extension movements. The introduction of joint misalignments in the weight support model significantly reduced the model errors, in terms of weight estimation, and enhanced the estimation reliability. The introduced control architecture reduced model tracking errors regardless of the condition. Weight support significantly decreased the activity of antigravity muscles, as expected, but increased the activity of elbow extensors because gravity is usually exploited by humans to accelerate a limb downwards. These findings suggest that an adaptive weight support controller could be envisioned to further minimize human effort in certain applications.

The Dorsal Striatum Energizes Motor Routines.

The dorsal striatum (dS) has been implicated in storing procedural memories and controlling movement kinematics. Since procedural memories are expressed through movements, the exact nature of the dS function has proven difficult to delineate. Here, we challenged rats in complementary locomotion-based tasks designed to alleviate this confound. Surprisingly, dS lesions did not impair the rats' ability to remember the procedure for the successful completion of motor routines. However, the speed and initiation of the reward-oriented phase of the routines were irreversibly altered by the dS lesion. Further behavioral analyses, combined with modeling in the optimal control framework, indicated that these kinematic alterations were well explained by an increased sensitivity to effort. Our work provides evidence supporting a primary role of the dS in modulating the kinematics of reward-oriented actions, a function that may be related to the optimization of the energetic costs of moving.

Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction.

Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb's mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.

Modularity underlying the performance of unusual locomotor tasks inspired by developmental milestones.

Motor behaviors are often hypothesized to be set up from the combination of a small number of modules encoded in the central nervous system. These modules are thought to combine such that a variety of motor tasks can be realized, from reproducible tasks such as walking to more unusual locomotor tasks that typically exhibit more step-by-step variability. We investigated the impact of step-by-step variability on the modular architecture of unusual tasks compared with walking. To this aim, 20 adults had to perform walking and two unusual modes of locomotion inspired by developmental milestones (cruising and crawling). Sixteen surface electromyography (EMG) signals were recorded to extract both spatial and temporal modules. Modules were extracted from both averaged and nonaveraged (i.e., single step) EMG signals to assess the significance of step-to-step variability when participants practiced such unusual locomotor tasks. The number of modules extracted from averaged data was similar across tasks, but a higher number of modules was required to reconstruct nonaveraged EMG data of the unusual tasks. Although certain walking modules were shared with cruising and crawling, task-specific modules were necessary to account for the muscle patterns underlying these unusual locomotion modes. These results highlight a more complex modularity (e.g., more modules) for cruising and crawling compared with walking, which was only apparent when the step-to-step variability of EMG patterns was considered. This suggests that considering nonaveraged data is relevant when muscle modularity is studied, especially in motor tasks with high variability as in motor development.NEW & NOTEWORTHY This study addresses the general question of modularity in locomotor control. We demonstrate for the first time the importance of intraindividual variability in the muscle modularity of unusual locomotor behaviors that exhibit greater step-by-step variability than standard walking. Crawling and cruising, the unusual locomotor modes considered, are based on a more complex modular organization than walking. More spatial and temporal modules, task specific or shared with walking modules, are needed to reconstruct muscle patterns.

Vigor of reaching, walking, and gazing movements: on the consistency of interindividual differences.

Movement vigor is an important feature of motor control that is thought to originate from cortico-basal ganglia circuits and processes shared with decision-making, such as temporal reward discounting. Accordingly, vigor may be related to one's relationship with time, which may, in turn, reflect a general trait-like feature of individuality. While significant interindividual differences of vigor have been typically reported for isolated motor tasks, little is known about the consistency of such differences across tasks and movement effectors. Here, we assessed interindividual consistency of vigor across reaching (both dominant and nondominant arm), walking, and gazing movements of various distances within the same group of 20 participants. Given distinct neural pathways and biomechanical specificities of each movement modality, a significant consistency would corroborate the trait-like aspect of vigor. Vigor scores for dominant and nondominant arm movements were found to be highly correlated across individuals. Vigor scores of reaching and walking were also significantly correlated across individuals, indicating that people who reach faster than others also tend to walk faster. At last, vigor scores of saccades were uncorrelated with those of reaching and walking, reaffirming that the vigor of stimulus-elicited eye saccades is distinct. These findings highlight the trait-like aspect of vigor for reaching movements with either arms and, to a lesser extent, walking.NEW & NOTEWORTHY Robust interindividual differences of movement vigor have been reported for arm reaching and saccades. Beyond biomechanics, personality trait-like characteristics have been proposed to account for those differences. Here, we examined for the first time the consistency of interindividual differences of vigor during dominant/nondominant arm reaching, walking, and gazing to assess the trait-like aspect of vigor. We found a significant consistency of vigor within our group of individuals for all tested tasks/effectors except saccades.

Vigour of self-paced reaching movement: cost of time and individual traits.

People usually move at a self-selected pace in everyday life. Yet, the principles underlying the formation of human movement vigour remain unclear, particularly in view of intriguing inter-individual variability. It has been hypothesized that how the brain values time may be the cornerstone of such differences, beyond biomechanics. Here, we focused on the vigour of self-paced reaching movement and assessed the stability of vigour via repeated measurements within participants. We used an optimal control methodology to identify a cost of time (CoT) function underlying each participant's vigour, considering a model of the biomechanical cost of movement. We then tested the extent to which anthropometric or psychological traits, namely boredom proneness and impulsivity, could account for a significant part of inter-individual variance in vigour and CoT parameters. Our findings show that the vigour of reaching is largely idiosyncratic and tend to corroborate a relation between the relative steepness of the identified CoT and boredom proneness, a psychological trait relevant to one's relationship with time in decision-making.