Background Step length asymmetry (SLA) is a common hallmark of gait post-stroke. Though conventionally viewed as a spatial deficit, SLA can result from differences in where the feet are placed relative to the body (spatial strategy), the timing between foot-strikes (step time strategy), or the velocity of the body relative to the feet (step velocity strategy). Objective The goal of this study was to characterize the relative contributions of each of these strategies to SLA. Methods We developed an analytical model that parses SLA into independent step position, step time, and step velocity contributions. This model was validated by reproducing SLA values for twenty-five healthy participants when their natural symmetric gait was perturbed on a split-belt treadmill moving at either a 2:1 or 3:1 belt-speed ratio. We then applied the validated model to quantify step position, step time, and step velocity contributions to SLA in fifteen stroke survivors while walking at their self-selected speed. Results SLA was predicted precisely by summing the derived contributions, regardless of the belt-speed ratio. Although the contributions to SLA varied considerably across our sample of stroke survivors, the step position contribution tended to oppose the other two – possibly as an attempt to minimize the overall SLA. Conclusions Our results suggest that changes in where the feet are placed or changes in interlimb timing could be used as compensatory strategies to reduce overall SLA in stroke survivors. These results may allow clinicians and researchers to identify patient-specific gait abnormalities and personalize their therapeutic approaches accordingly.

SUMMARY In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual feedback dependent process that monitors learning and corrects residual errors, but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change.

Bottom-up self-assembly offers a means to synthesize materials with desirable structural and functional properties that cannot easily be fabricated by other techniques. An improved understanding of the structural pathways and mechanisms by which self-assembling materials spontaneously form from their constituent building blocks is of value in understanding the fundamental principles of assembly and in guiding inverse building block design. We present an approach to infer systematically assembly pathways and mechanisms by nonlinear data mining of molecular simulation trajectories using diffusion maps. We have validated our methodology in applications to Brownian dynamics simulations of the assembly of anisotropic "patchy colloids" into polyhedral aggregates. For particles designed to form tetrahedral aggregates, we identify two divergent assembly pathways leading to chains of interlocking dimers and tetramers and chains of interlocking trigonal planar trimers. For particles designed to assemble icosahedral aggregates, our approach recovers two distinct assembly pathways corresponding to monomeric addition and budding from a disordered liquid phase. These assembly routes were previously reported by inspection of simulation trajectories by Wilber et al. ( J. Chem. Phys. 2007, 127, 085106 ; J. Chem. Phys. 2009, 131, 175102 ), validating the capacity of our approach to systematically recover assembly mechanisms previously discernible only by trajectory visualization.

BackgroundSoft exosuits are a recent approach for assisting human locomotion, which apply assistive torques to the wearer through functional apparel. Over the past few years, there has been growing recognition of the importance of control individualization for such gait assistive devices to maximize benefit to the wearer. In this paper, we present an updated version of autonomous multi-joint soft exosuit, including an online parameter tuning method that customizes control parameters for each individual based on positive ankle augmentation power.MethodsThe soft exosuit is designed to assist with plantarflexion, hip flexion, and hip extension while walking. A mobile actuation system is mounted on a military rucksack, and forces generated by the actuation system are transmitted via Bowden cables to the exosuit. The controller performs an iterative force-based position control of the Bowden cables on a step-by-step basis, delivering multi-articular (plantarflexion and hip flexion) assistance during push-off and hip extension assistance in early stance. To individualize the multi-articular assistance, an online parameter tuning method was developed that customizes two control parameters to maximize the positive augmentation power delivered to the ankle. To investigate the metabolic efficacy of the exosuit with wearer-specific parameters, human subject testing was conducted involving walking on a treadmill at 1.50 m s− 1 carrying a 6.8-kg loaded rucksack. Seven participants underwent the tuning process, and the metabolic cost of loaded walking was measured with and without wearing the exosuit using the individualized control parameters.ResultsThe online parameter tuning method was capable of customizing the control parameters, creating a positive ankle augmentation power map for each individual. The subject-specific control parameters and resultant assistance profile shapes varied across the study participants. The exosuit with the wearer-specific parameters significantly reduced the metabolic cost of load carriage by 14.88 ± 1.09% (P = 5 × 10− 5) compared to walking without wearing the device and by 22.03 ± 2.23% (P = 2 × 10− 5) compared to walking with the device unpowered.ConclusionThe autonomous multi-joint soft exosuit with subject-specific control parameters tuned based on positive ankle augmentation power demonstrated the ability to improve human walking economy. Future studies will further investigate the effect of the augmentation-power-based control parameter tuning on wearer biomechanics and energetics.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0410-y) contains supplementary material, which is available to authorized users.

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data. We apply this technique to the nonequilibrium self-assembly of metallodielectric Janus colloids in an oscillating electric field, and quantify the impact of field strength, oscillation frequency, and salt concentration on the dominant assembly pathways and terminal aggregates. This combined computational and experimental framework furnishes new understanding of self-assembling systems, and quantitatively informs rational engineering of experimental conditions to drive assembly along desired aggregation pathways.

Long AW, Roemmich RT, Bastian AJ. Blocking trial-by-trial error correction does not interfere with motor learning in human walking. J Neurophysiol 115: 2341-2348, 2016. First published February 24, 2016 doi:10.1152/jn.00941.2015.-Movements can be learned implicitly in response to new environmental demands or explicitly through instruction and strategy. The former is often studied in an environment that perturbs movement so that people learn to correct the errors and store a new motor pattern. Here, we demonstrate in human walking that implicit learning of foot placement occurs even when an explicit strategy is used to block changes in foot placement during the learning process. We studied people learning a new walking pattern on a split-belt treadmill with and without an explicit strategy through instruction on where to step. When there is no instruction, subjects implicitly learn to place one foot in front of the other to minimize step-length asymmetry during split-belt walking, and the learned pattern is maintained when the belts are returned to the same speed, i.e., postlearning. When instruction is provided, we block expression of the new foot-placement pattern that would otherwise naturally develop from adaptation. Despite this appearance of no learning in foot placement, subjects show similar postlearning effects as those who were not given any instruction. Thus locomotor adaptation is not dependent on a change in action during learning but instead can be driven entirely by an unexpressed internal recalibration of the desired movement. adaptation; feedback; gait; motor learning; walking THE HUMAN NERVOUS SYSTEM USES a repertoire of learning mechanisms to change walking patterns to account for a variety of environments and situations. For example, we explicitly think about where to place our feet when stepping on stones in a river but adapt more implicitly to walking in snow. We currently do not understand how these explicit and implicit processes interact when they are put in conflict or engaged toward the same learning goal. For example, if an explicit strategy is used to block the expression of implicit learning, does the latter still occur? Here, we studied the interactions between these explicit and implicit processes using a strategy via visual feedback and a split-belt treadmill, respectively.When subjects walk on a split-belt treadmill with one belt moving faster than the other, they learn a new gait pattern over hundreds of steps by changing where and when they place their feet on the treadmill Long et al. 2015;Reisman et al. 2005). When the treadmill belts are then returned to the same speed, postlearning effects are observed such that subjects retain much of the newly learned spatial pattern that then decays over a couple hundred steps (Reisman et al. 2005). This learning is largely implicit, particularly when the perturbation is gradually introduced (Sawers et al. 2013; TorresOviedo and Bastian 2012): subjects are often unaware that there is a perturbation for much of the learning period. Additi...

Stroke-induced hemiparetic gait is characteristically asymmetric and metabolically expensive. Weakness and impaired control of the paretic ankle contribute to reduced forward propulsion and ground clearance - walking subtasks critical for safe and efficient locomotion. Targeted gait interventions that improve paretic ankle function after stroke are therefore warranted. We have developed textile-based, soft wearable robots that transmit mechanical power generated by off-board or body-worn actuators to the paretic ankle using Bowden cables (soft exosuits) and have demonstrated the exosuits can overcome deficits in paretic limb forward propulsion and ground clearance, ultimately reducing the metabolic cost of hemiparetic walking. This study elucidates the biomechanical mechanisms underlying exosuit-induced reductions in metabolic power. We evaluated the relationships between exosuit-induced changes in the body center of mass (COM) power generated by each limb, individual joint power and metabolic power. Compared with walking with an exosuit unpowered, exosuit assistance produced more symmetrical COM power generation during the critical period of the step-to-step transition (22.4±6.4% more symmetric). Changes in individual limb COM power were related to changes in paretic (=0.83, 0.004) and non-paretic (0.73, 0.014) ankle power. Interestingly, despite the exosuit providing direct assistance to only the paretic limb, changes in metabolic power were related to changes in non-paretic limb COM power (=0.80, 0.007), not paretic limb COM power (0.05). These findings contribute to a fundamental understanding of how individuals post-stroke interact with an exosuit to reduce the metabolic cost of hemiparetic walking.