Locomotor Tasks Research Articles

Human in vivo talocrural contributions to ankle joint complex kinematics during walking, running, and hopping.

The human ankle joint complex, consisting of calcaneus, talus, and tibia, is often simplified as a single functional ankle joint, neglecting the motion of the talus. Understanding the individual contributions of the talus and calcaneus is crucial for comprehending ankle joint complex function in healthy populations, and alterations in function that may exist in clinical conditions. To achieve accurate bone kinematics, high-resolution biplanar videoradiography was used with participants engaged in walking and running (n=9) and hopping (n=9) with no overlap in participants. The rotation axes for the calcaneus and talus were analysed relative to the tibia over the ankle joint dorsi-/plantar flexion phases. Contributions of the talocrural joint to overall ankle joint complex function were measured by comparing the range of motion (talus relative to calcaneus). Most of the sagittal plane motion in the ankle joint complex (80%) occurred in the talocrural joint, with the subtalar joint contributing 20%. Rotation of the calcaneus about the tibia dominated frontal and transverse plane motion during hopping. Although surprisingly large ranges of talus motion were also observed in these planes (>5deg), indicating notable inter-subject variability and that the talocrural joint is not a simple hinge joint. The study highlights the importance of directly quantifying talus motion to understand ankle joint complex function. The results showed opposing talus and calcaneus movements for many participants during different locomotor tasks, which may influence the magnitude and distribution of subtalar joint loading. In addition, the large out-of-sagittal plane movements and inter-subject variability in talus and calcaneus motion may further emphasize the need for personalised models to investigate treatments for ankle pathologies.

Establishing thresholds for swing transparency at the knee during gait to inform exoskeleton design.

Knee exoskeletons have been developed to assist, stabilize, or improve human movement or recovery. However, exoskeleton designers must implement transparency (i.e., get out of the way) modes during the swing phase of locomotor tasks to avoid impeding movement. The problem is that it is not understood how sensitive people are to small knee torques or what level of knee impedance is acceptable (sufficiently transparent) during swing phase. Here, we (i) characterized the biomechanical consequences of knee stiffness and damping during swing, and (ii) leveraged user perceptions of being impeded and toe clearance to define transparency thresholds, below which the participants were sufficiently unimpeded during the swing phase of gait. We conducted a series of human subject experiments that involved walking and stair ascent/descent while wearing a modified knee brace with five stiffness values ranging from 0 to 4 Nm/rad and five damping values ranging from 0 to 0.77 Nm/rad/s. We measured changes to lower limb kinematics, knee flexor muscle activity, and participants' perception of being impeded during swing. Kinematics, muscle activity, and perceived impedance all changed in response to added stiffness and damping. For stiffness, we found the median transparency thresholds for walking and stairs to be 1.76 Nm/rad and 2.95 Nm/rad, respectively, which corresponds to peak knee moments during swing of around 2.3 and 5 Nm. For damping, we found the median transparency threshold for walking and stairs to be about the same, 0.29 Nm/rad/s, which corresponds to peak knee moments during swing of around 2.3 Nm. These values provide useful benchmarks for defining quantitative design requirements for knee exoskeletons intended for locomotor activities.

AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility

The role of the human ankle joint in activities of daily living, including walking, maintaining balance, and participating in sports, is of paramount importance. Ankle joint dorsiflexion and plantarflexion functionalities mainly account for ground clearance and propulsion power generation during locomotion tasks, where those functionalities are driven by the contraction of ankle joint skeleton muscles. Studies of corresponding muscle contractility during ankle dynamic functions will facilitate us to better understand the joint torque/power generation mechanism, better diagnose potential muscular disorders on the ankle joint, or better develop wearable assistive/rehabilitative robotic devices that assist in community ambulation. This data descriptor reports a new dataset that includes the ankle joint kinematics/kinetics, associated muscle surface electromyography, and ultrafast ultrasound images with various annotations, such as pennation angle, fascicle length, tissue displacements, echogenicity, and muscle thickness, of ten healthy participants when performing volitional isometric, isokinetic, and dynamic ankle joint functions (walking at multiple treadmill speeds, including 0.50 m/s, 0.75 m/s, 1.00 m/s, 1.25 m/s, and 1.50 m/s). Data were recorded by a research-use ultrasound machine, a self-designed ankle testbed, an inertia measurement unit system, a Vicon motion capture system, a surface electromyography system, and an instrumented treadmill. The descriptor in this work presents the results of a data curation or collection exercise from previous works, rather than describing a novel primary/experimental data collection.

Learning agility and adaptive legged locomotion via curricular hindsight reinforcement learning

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (i) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor running speeds up to 3.45 m/s, and a maximum yaw rate of 3.2 rad/s. This system produces adaptive behaviors responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.

Totipotent neural controllers for modular soft robots: Achieving specialization in body–brain co-evolution through Hebbian learning

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular “totipotency”, a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robot (VSR), where both the body, i.e., the arrangement of voxels, and the brain, i.e., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react in different ways to the same set of stimuli, i.e., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of Artificial Intelligence (AI) and applications thereof, such as modular robotics and multi-agent systems.

Synchronization-based fusion of EEG and eye blink signals for enhanced decoding accuracy

Decoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.

Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension

Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective. Humans are a prime example, deviating from perfect symmetry in terms of appearance and cognitive biases (e.g. having a dominant hand). Nevertheless, our brain can easily overcome these imperfections and efficiently adapt to symmetrical tasks. The driving motivation behind this work lies in capturing this ability through reinforcement learning. To this end, we introduce Adaptive Symmetry Learning (ASL) — a model-minimization actor-critic extension that addresses incomplete or inexact symmetry descriptions by adapting itself during the learning process. ASL consists of a symmetry fitting component and a modular loss function that enforces a common symmetric relation across all states while adapting to the learned policy. The performance of ASL is compared to existing symmetry-enhanced methods in a case study involving a four-legged ant model for multidirectional locomotion tasks. The results show that ASL can recover from large perturbations and generalize knowledge to hidden symmetric states. It achieves comparable or better performance than alternative methods in most scenarios, making it a valuable approach for leveraging model symmetry while compensating for inherent perturbations.

Induced discomfort promotes executive function while walking with(out) an artificial neuromuscular impairment.

Neuromotor disorders can degrade one's ability to locomote and attend to salient stimuli in the environment. Many disorders are physiologically complex, making it difficult to tease apart interactions between motor adaptation and executive function processes. We address this challenge by giving participants a controlled artificial impairment, using electrical stimulation to produce an uncomfortable disruption in normal muscular coordination during locomotion. While adapting to this gait perturbation, participants performed an executive function task containing neutral and affectively charged stimuli. The artificial impairment was counterbalanced against control and sham (discomfort-only stimulation) walking conditions. Our twofold hypothesis that discomfort would selectively tax hot, emotionally charged executive function and motor adaptation would challenge cold, logical executive function was not supported. However, we found that the discomfort experienced with both stimulation conditions improved participants' ability to inhibit distracting information, enhancing this aspect of executive function, and the effect did not depend on whether the task was affectively charged. Moderate discomfort during physical activity may have improved inhibitory control by increasing arousal, a known factor mediating executive function. These results show that using a sensorimotor perturbation that acts internally and bridges multiple physiological domains, including discomfort, can reveal effects not seen with purely environmental manipulations. The broader implications are that when high cognitive performance is needed during physical activity, it may be beneficial to, quite literally, operate outside one's comfort zone.NEW & NOTEWORTHY When locomotor and cognitive tasks compete for shared neural resources, cognitive-motor interference may impair performance in both domains. To understand how impairments that cause pain or change neuromotor control impact cognition during locomotion, we gave healthy adults an uncomfortable, artificial neuromuscular impairment while they walked and completed a task dependent on ignoring distracting stimuli. We found that discomfort enhanced participants' ability to ignore distractions, providing new insight into the mediators of cognition during impaired movement.

Activity Identification, Classification, and Representation of Wheelchair Sport Court Tasks: A Method Proposal.

Monitoring player mobility in wheelchair sports is crucial for helping coaches understand activity dynamics and optimize training programs. However, the lack of data from monitoring tools, combined with a lack of standardized processing approaches and ineffective data presentation, limits their usability outside of research teams. To address these issues, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements, utilizing kinematic data from standard wheelchair mobility tests. Each participant's wheelchair was equipped with inertial measurement units-two on the wheel axes and one on the frame. A total of 36 wheelchair tennis and badminton players completed at least one of three proposed tests: the star test, the figure-of-eight test, and the forward/backward test. Locomotor tasks were identified using a five-step procedure involving data reduction, symbolic approximation, and logical pattern searching. This method successfully identified 99% of locomotor tasks for the star test, 95% for the figure-of-eight test, and 100% for the forward/backward test. The proposed method offers a valuable tool for the simple and clear identification and representation of locomotor tasks over extended periods. Future research should focus on applying this method to wheelchair court sports matches and daily life scenarios.

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent's behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

Different types of mastoid vibrations affect the amount and structure of minimum toe clearance variability differently

BackgroundThe majority of research primarily examines the role of the vestibular system in regulating balance by assessing gait parameters in the transverse plane while neglecting those in the sagittal plane. The present study aimed to examine the impact of various forms of mastoid vibration (MV) on minimum toe clearance (MTC) and its pattern of variability. This study proposed two hypotheses: 1) the application of MV reduced the MTC, and 2) the application of different forms of MV influenced the amount and structure of MTC variability. MethodsA total of twenty young adults participated in this study. A high-resolution motion capture system with eight cameras captured the minimum toe clearance. Three locomotor tasks were randomly assigned to these young participants: 1) walking normally on the treadmill, 2) walking with unilateral MV, and 3) walking with bilateral MV. The dependent variables were the mean of MTC, the amount, and the structure of MTC variability. The amount of MTC variability was calculated by the coefficient of variation represented, and the structure of MTC variability was measured using a sample entropy measure for a total of 200 MTCs. ResultsApplying unilateral and bilateral MV decreased the MTC significantly (-1.6 %, p = 0.038; −4.3 %, p < 0.001) compared to normal walking. Also, applying unilateral MV increased the amount (11.8 %, p = 0.001) and structure of MTC variability (14.3 %, p < 0.001) compared to normal walking. However, applying bilateral MV decreased the amount (-8.8 %, p = 0.001) and structure of MTC variability (-9.0 %, p < 0.001) compared to regular walking. ConclusionAlthough the statistical differences in MTC and MTC variability were observed in the present study, the mean differences among the different MV conditions were relatively small, thereby requiring meticulous deliberation when extrapolating the results when implementing this MTC in the pathological cohort.

Variational analysis of sensory feedback mechanisms in powerstroke–recovery systems

Although the raison d’etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke–recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate—such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance–sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation–inhibition property of feedback mechanisms determines the sensitivity pattern while the activation–inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems.

TendrilBot: Modular soft robot with versatile radial grasping and locomotion capabilities

This paper introduces TendrilBot, a novel modular soft robot designed for versatile manipulation, stiffness modulation, and locomotion tasks. Built upon previously developed modular units, the TendrilBot consists of multiple actuators connected linearly in linear, cylindrical, or helix configurations. Cylindrical configurations (single row or stacked) enable the robot to wrap around objects for grasping and manipulation in a single row or stacked configuration when interconnected with the magnets on sides of the units. Additionally, such configurations further facilitate internal grasping within hollow tubes or objects holding them with its reconfigurable shape. Both wrapping and internal grasping are experimentally validated to demonstrate radial stiffness modulation and squeezing capabilities. By further rearranging these modular units, TendrilBot can transform into a robotic arm with four or more degrees of freedom, significantly enhancing its manipulative capabilities. In addition, two types of locomotion are demonstrated. When connected in a linear configuration, TendrilBot exhibits locomotion through a sinusoidal motion pattern, similar to sidewinding. When connected in a circular configuration, TendrilBot can achieve locomotion via rolling with steering capabilities, utilizing its cylindrical or helical shape, respectively. Presented are detailed design configurations and characterization of TendrilBot functionalities and capabilities. In addition, we developed a data-driven model that can accurately capture bending of the modular soft actuators, which was validated by the experimental results. Comparison with other similar modular soft robots is provided and discussed. Extensive experimental validations are performed to showcase TendrilBot's potential for diverse applications in the field of modular soft robotics.

Presentation of multiple task instructional sets impacts auditory Stroop performance during dual task locomotion.

The auditory Stroop is a modification of the classic Stroop paradigm commonly used in dual-task research when the motor task requires the visual system. Despite its use, there are gaps in our understanding of this tool. For example, in visual/auditory Stroop paradigms, neutral cues irrelevant to the required response, which theoretically cause less interference/facilitation, are used to elucidate effects of visual/auditory demands on neural processes. Specifically, in auditory Stroop paradigms the use and choice of neutral cue words is inconsistent. To address these gaps, we instrumented participants with kinematic markers and a digital microphone and asked them to respond to auditory Stroop cues and neutral cue words consisting of either one or two syllables, while simultaneously performing an unobstructed locomotor task. Two blocks of trials were collected. In one block, participants had prior knowledge that either an auditory Stroop or a neutral word stimulus would be presented (Known); a second block presented both types of cognitive cues in a random order to participants (Mixed). We observed main effects of cognitive task (neutral, incongruent, congruent) and instructional set (Known, Mixed) on response times, but not on center of mass velocity. Also, more time was required to verbally respond to an incongruent compared to congruent or neutral task across all conditions, and neutral task words with one syllable resulted in longer response times compared to two syllable neutral words. We recommend that researchers include neutral cues when using the auditory Stroop test and to carefully consider their neutral word choice.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective.

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

Leaning-Based Interfaces Improve Simultaneous Locomotion and Object Interaction in VR Compared to the Handheld Controller.

Physical walking is often considered the gold standard for VR travel whenever feasible. However, limited free-space walking areas in the real-world do not allow exploring larger-scale virtual environments by actual walking. Therefore, users often require handheld controllers for navigation, which can reduce believability, interfere with simultaneous interaction tasks, and exacerbate adverse effects such as motion sickness and disorientation. To investigate alternative locomotion options, we compared handheld Controller (thumbstick-based) and physical walking versus a seated (HeadJoystick) and standing/stepping (NaviBoard) leaning-based locomotion interface, where seated/standing users travel by moving their head toward the target direction. Rotations were always physically performed. To compare these interfaces, we designed a novel simultaneous locomotion and object interaction task, where users needed to keep touching the center of upward moving target balloons with their virtual lightsaber, while simultaneously staying inside a horizontally moving enclosure. Walking resulted in the best locomotion, interaction, and combined performances while the controller performed worst. Leaning-based interfaces improved user experience and performance compared to Controller, especially when standing/stepping using NaviBoard, but did not reach walking performance. That is, leaning-based interfaces HeadJoystick (sitting) and NaviBoard (standing) that provided additional physical self-motion cues compared to controller improved enjoyment, preference, spatial presence, vection intensity, motion sickness, as well as performance for locomotion, object interaction, and combined locomotion and object interaction. Our results also showed that less embodied interfaces (and in particular the controller) caused a more pronounced performance deterioration when increasing locomotion speed. Moreover, observed differences between our interfaces were not affected by repeated interface usage.

Prospective control of steering through multiple waypoints.

Some locomotor tasks involve steering at high speeds through multiple waypoints within cluttered environments. Although in principle actors could treat each individual waypoint in isolation, skillful performance would seem to require them to adapt their trajectory to the most immediate waypoint in anticipation of subsequent waypoints. To date, there have been few studies of such behavior, and the evidence that does exist is inconclusive about whether steering is affected by multiple future waypoints. The present study was designed to address the need for a clearer understanding of how humans adapt their steering movements in anticipation of future goals. Subjects performed a simulated drone flying task in a forest-like virtual environment that was presented on a monitor while their eye movements were tracked. They were instructed to steer through a series of gates while the distance at which gates first became visible (i.e., lookahead distance) was manipulated between trials. When gates became visible at least 1-1/2 segments in advance, subjects successfully flew through a high percentage of gates, rarely collided with obstacles, and maintained a consistent speed. They also approached the most immediate gate in a way that depended on the angular position of the subsequent gate. However, when the lookahead distance was less than 1-1/2 segments, subjects followed longer paths and flew at slower, more variable speeds. The findings demonstrate that the control of steering through multiple waypoints does indeed depend on information from beyond the most immediate waypoint. Discussion focuses on the possible control strategies for steering through multiple waypoints.

Spinal maps of motoneuron activity during human locomotion: neuromechanical considerations.

The spatial segmental location of motoneurons in the human spinal cord is influenced by both evolutionary and functional principles tending to optimize motor control, reflex integration, and adaptation to the demands of movement. Bearing in mind the biomechanics of limb muscles, it is logical to examine how motoneuron activity clusters functionally during typical daily activities like walking. This article provides a summary of advancements in the study of spinal maps of motoneuron activation during human locomotion by reviewing data gathered over ∼20years. The effects of child development, aging, and neurological disorders show the salient characteristics of spinal segmental activity during different human locomotor tasks and conditions. By exploiting the neuromechanics of the spinal motor circuits, that is, the link between motoneuron activity and gait mechanics, neuroprosthetics and other focused treatments may better help individuals with locomotor impairments.

Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations.

Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of neural networks. In neuroevolution with topological evolution, handling crossover is challenging because of the competing conventions problem. Mutation-based evolving artificial neural network is an alternative topology and weights neuroevolution approach that omits crossover and uses only mutations for genetic variation. This study enhances the performance of mutation-based evolving artificial neural network in two ways. First, the mutation step size controlling the magnitude of the parameter perturbation is automatically adjusted by a self-adaptive mutation mechanism, enabling a balance between exploration and exploitation during the evolution process. Second, the structural mutation probabilities are automatically adjusted depending on the network size, preventing excessive expansion of the topology. The proposed methods are compared with conventional neuroevolution algorithms using locomotion tasks provided in the OpenAI Gym benchmarks. The results demonstrate that the proposed methods with the self-adaptive mutation mechanism can achieve better performance. In addition, the adjustment of structural mutation probabilities can mitigate topological bloat while maintaining performance.

Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke.

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. We utilized a multi-rate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since post-stroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.