Motor primitives--new data and future questions

High risk of falling in elderly with hallux valgus evaluated by muscle and kinematic synergistic analysis

BACKGROUND

Musculoskeletal disorders often present with abnormalities in neuromuscular control. Hallux valgus (HV) is considered a risk factor for falls in the elderly, and it is one of the foot conditions most associated with falls. The onset of falls is associated with neuromuscular modulation of the lower extremity. However, there is a lack of clarity regarding the muscle and kinematic synergy patterns i.e., neuromuscular control in elderly HV.

RESEARCH QUESTION

How the changes in neuromuscular control in elderly HV based on muscle and kinematic synergies analysis?

METHODS

The study included three groups: young controls (YC), elderly controls (EC), and elderly hallux valgus (HVE). All participants were assessed for gait at their natural walking speed. Data were analyzed using non-negative matrix factorization (NNMF) for electromyography (EMG) and joint motion to compare muscle and kinematic synergies across the groups. The center of plantar pressure (COP) was used to evaluate self-balancing ability.

RESULTS

The EC group demonstrated the additional activation of calf muscle groups accompanied by decreased ankle motion and increased hip abduction. Compared to the EC group, the HVE group required more thigh flexor muscle groups to compensate for the lack of function of the ankle movements during gait and showed decreased hip abduction but increased knee flexion. During gait, the COP were significantly large than YC and EC groups (P < 0.05) in the HVE group.

SIGNIFICANCE

Our finding indicate that the elderly individuals with hallux valgus exhibit under-activated calf muscles around foot joints, and despite compensations from thigh muscles, they maintain an imbalance and increased risk of falls. This study will help to evaluate HVE control strategies and provide personalized treatment based on these vulnerabilities to reduce the risk of HVE falls.

Effects of spinal transection and locomotor speed on muscle synergies of the cat hindlimb

It has been suggested that during locomotion, the nervous system controls movement by activating groups of muscles, or muscle synergies. Analysis of muscle synergies can reveal the organization of spinal locomotor networks and how it depends on the state of the nervous system, such as before and after spinal cord injury, and on different locomotor conditions, including a change in speed. The goal of this study was to investigate the effects of spinal transection and locomotor speed on hindlimb muscle synergies and their time-dependent activity patterns in adult cats. EMG activities of 15 hindlimb muscles were recorded in nine adult cats of either sex during tied-belt treadmill locomotion at speeds of 0.4, 0.7 and 1.0 m/s before and after recovery from a low thoracic spinal transection. We determined EMG burst groups using cluster analysis of EMG burst onset and offset times and muscle synergies using non-negative matrix factorization (NNMF). We found five major EMG burst groups and five muscle synergies in each of six experimental conditions (2 states × 3 speeds). In each case, the synergies accounted for at least 90% of muscle EMG variance. Both spinal transection and locomotion speed modified subgroups of EMG burst groups and the composition and activation patterns of selected synergies. However, these changes did not modify the general organization of muscle synergies. Based on the obtained results, we propose an organization for a pattern formation network of a two-level central pattern generator that can be tested in neuromechanical simulations of spinal circuits controlling cat locomotion. KEY POINTS: Analysis of muscle synergies during locomotion can reveal the organization of spinal locomotor networks. We recorded EMG activity of 15 hindlimb muscles in cats locomoting on a treadmill at speeds 0.4, 0.7 and 1.0 m/s before and after recovery from spinal cord transection at low thoracic level. We found five muscle synergies in all six experimental conditions (2 spinal states x 3 speeds) that include two flexor synergies operating in the swing phase and three extensor synergies operating in the stance phase. Major features of found synergies (the number, muscle composition and activation patterns) were not substantially affected by spinal transection and locomotion speed, suggesting that spinal control mechanism operates muscle synergies. Based on the obtained results, we proposed an organization of a pattern formation network of a two-level central pattern generator controlling locomotor activity of hindlimb muscles.

The central nervous system adjusts muscle synergy structure and tightly controls rollator-supported transitions between sitting and standing

BACKGROUND

Older individuals are at risk of falling. Assistive devices like rollators help to reduce that risk, especially by compensating for decreased leg muscle strength and balance problems. Paradoxically, rollators have been found to be a fall risk as well as being difficult to use. To investigate the causes, this study examines how different levels of rollator support (no assistance, light touch, and full support) and balance demands (standard lab floor, balance pads) affect movement coordination during standing up and sitting down movements.

METHODS

Twenty young participants stood up and sat down while full-body kinematics and muscle activity (30 channels) were recorded. Participants stood up and sat down using different movement strategies (e.g., forward leaning, hybrid, and vertical rise standing up movement strategies). For each movement strategy, spatial and temporal muscle synergies were extracted from the muscle activity patterns. Temporal muscle synergies provided a more compact, low-dimensional representation than spatial muscle synergies, so they were subsequently clustered with k-means++. The activation duration of the temporal muscle synergies was assessed with full-width at half-maximum at the main peak. Multivariate linear mixed models were used to investigate if the muscle weightings associated with the temporal muscle synergies differed across the support conditions.

RESULTS

The timings of the temporal muscle synergy activations, but not the shape, differed across the movement strategies for both types of movement. Across all tasks, temporal muscle synergies showed a narrower width of activation around the time of seat-off and seat-on than at the movement start and end. No support-specific temporal muscle synergies were found, but lower limb muscle weightings decreased while upper-limb muscle weightings increased with increased support.

CONCLUSION

The narrow shape of the temporal synergy activation profiles suggests that the central nervous system controls the movements tightly, especially around seat-off and seat-on and in challenging conditions with increased balance demands. Furthermore, rollator support increases the weightings of upper body and decreases the weightings of lower limb muscles, especially around seat-off and seat-on. Future studies may further investigate how the loss of tight movement control may cause falls in older individuals.

Biomechanical models in the lower-limb exoskeletons development: a review

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models.

Useful and Useless Misnomers in Motor Control

This article addresses the issue of using terms and concepts in motor control that are ill-defined, undefined, and/or imported from nonbiological fields. In many of such cases, the discourse turns nonscientific and unproductive. Some of such terms are potentially useful but need to be properly and exactly defined. Other terms seem to be misleading and nonfixable. There is also an intermediate group with terms that may or may not be useful if defined properly. The paper presents three examples per group: "reflex," "synergy," and "posture" versus "motor program," "efference copy," and "internal model" versus "muscle tone," "stiffness and impedance," and "redundancy." These terms are analyzed assuming that motor control is a branch of natural science, which must be analyzed using laws of nature, not a subfield of the control theory. In the discussion, we also accept the framework of the theory of movement control with spatial referent coordinates as the only example built on laws of nature with clearly formulated physical and physiological nature of the control parameters.

Neuromechanical Circuits of the Spinal Motor Apparatus

The evolution of mechanisms for terrestrial locomotion has resulted in multi-segmented limbs that allow navigation on irregular terrains, changing of direction, manipulation of external objects, and control over the mechanical properties of limbs important for interaction with the environment, with corresponding changes in neural pathways in the spinal cord. This article is focused on the organization of these pathways, their interactions with the musculoskeletal system, and the integration of these neuromechanical circuits with supraspinal mechanisms to control limb impedance. It is argued that neural pathways from muscle spindles and Golgi tendon organs form a distributive impedance controller in the spinal cord that controls limb impedance and coordination during responses to external disturbances. These pathways include both monosynaptic and polysynaptic components. Autogenic, monosynaptic pathways serve to control the spring-like properties of muscles preserving the nonlinear relationship between stiffness and force. Intermuscular monosynaptic pathways compensate for inertial disparities between the inertial properties of limb segments and help to control inertial coupling between joints and axes of rotation. Reciprocal inhibition controls joint stiffness in conjunction with feedforward cocontraction commands. Excitatory force feedback becomes operational during locomotion and increases muscular stiffness to accommodate the higher inertial loads. Inhibitory force feedback is widely distributed among muscles. It is integrated with excitatory pathways from muscle spindles and Golgi tendon organs to determine limb stiffness and interjoint coordination during interactions with the environment. The intermuscular distribution of force feedback is variable and serves to modulate limb stiffness to meet the physical demands of different motor tasks. © 2024 American Physiological Society. Compr Physiol 14:5789-5838, 2024.

Role of the Cerebellum in the Construction of Functional and Geometrical Spaces

The perceptual and motor systems appear to have a set of movement primitives that exhibit certain geometric and kinematic invariances. Complex patterns and mental representations can be produced by (re)combining some simple motor elements in various ways using basic operations, transformations, and respecting a set of laws referred to as kinematic laws of motion. For example, point-to-point hand movements are characterized by straight hand paths with single-peaked-bell-shaped velocity profiles, whereas hand speed profiles for curved trajectories are often irregular and more variable, with speed valleys and inflections extrema occurring at the peak curvature. Curvature and speed are generically related by the 2/3 power law. Mathematically, such laws can be deduced from a combination of Euclidean, affine, and equi-affine geometries, whose neural correlates have been partially detected in various brain areas including the cerebellum and the basal ganglia. The cerebellum has been found to play an important role in the control of coordination, balance, posture, and timing over the past years. It is also assumed that the cerebellum computes forward internal models in relationship with specific cortical and subcortical brain regions but its motor relationship with the perceptual space is unclear. A renewed interest in the geometrical and spatial role of the cerebellum may enable a better understanding of its specific contribution to the action-perception loop and behavior's adaptation. In this sense, we complete this overview with an innovative theoretical framework that describes a possible implementation and selection by the cerebellum of geometries adhering to different mathematical laws.

Quantifying spontaneous infant movements using state-space models

Over the first few months after birth, the typical emergence of spontaneous, fidgety general movements is associated with later developmental outcomes. In contrast, the absence of fidgety movements is a core feature of several neurodevelopmental and cognitive disorders. Currently, manual assessment of early infant movement patterns is time consuming and labour intensive, limiting its wider use. Recent advances in computer vision and deep learning have led to the emergence of pose estimation techniques, computational methods designed to locate and track body points from video without specialised equipment or markers, for movement tracking. In this study, we use automated markerless tracking of infant body parts to build statistical models of early movements. Using a dataset of infant movement videos (n = 486) from 330 infants we demonstrate that infant movement can be modelled as a sequence of eight motor states using autoregressive, state-space models. Each, motor state Is characterised by specific body part movements, the expression of which varies with age and differs in infants at high-risk of poor neurodevelopmental outcome.

De novo sensorimotor learning through reuse of movement components

From tying one's shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred less robustly to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results suggest that the novel controllers acquired during de novo learning can, in some circumstances, be constructed from components of existing controllers.

EFFECTS OF SPINAL TRANSECTION AND LOCOMOTOR SPEED ON MUSCLE SYNERGIES OF THE CAT HINDLIMB

It was suggested that during locomotion, the nervous system controls movement by activating groups of muscles, or muscle synergies. Analysis of muscle synergies can reveal the organization of spinal locomotor networks and how it depends on the state of the nervous system, such as before and after spinal cord injury, and on different locomotor conditions, including a change in speed. The goal of this study was to investigate the effects of spinal transection and locomotor speed on hindlimb muscle synergies and their time-dependent activity patterns in adult cats. EMG activities of 15 hindlimb muscles were recorded in 9 adult cats of either sex during tied-belt treadmill locomotion at speeds of 0.4, 0.7, and 1.0 m/s before and after recovery from a low thoracic spinal transection. We determined EMG burst groups using cluster analysis of EMG burst onset and offset times and muscle synergies using non-negative matrix factorization. We found five major EMG burst groups and five muscle synergies in each of six experimental conditions (2 states × 3 speeds). In each case, the synergies accounted for at least 90% of muscle EMG variance. Both spinal transection and locomotion speed modified subgroups of EMG burst groups and the composition and activation patterns of selected synergies. However, these changes did not modify the general organization of muscle synergies. Based on the obtained results, we propose an organization for a pattern formation network of a two-level central pattern generator that can be tested in neuromechanical simulations of spinal circuits controlling cat locomotion.

Comparative analysis of motor skill acquisition in a novel bimanual task: the role of mental representation and sensorimotor feedback

Introduction

This study investigates the multifaceted nature of motor learning in a complex bimanual task by examining the interplay between mental representation structures, biomechanics, tactile pressure, and performance. We developed a novel maze game requiring participants to maneuver a rolling sphere through a maze, exemplifying complex sequential coordination of vision and haptic control using both hands. A key component of this study is the introduction of cognitive primitives, fundamental units of cognitive and motor actions that represent specific movement patterns and strategies.

Methods

Participants were divided into two groups based on initial performance: poor performers (PPG) and good performers (GPG). The experimental setup employed motion capture and innovative tactile sensors to capture a detailed multimodal picture of the interaction process. Our primary aims were to (1) assess the effects of daily practice on task performance, biomechanics, and tactile pressure, (2) examine the relationship between changes in mental representation structures and skill performance, and (3) explore the interplay between biomechanics, tactile pressure, and cognitive representation in motor learning.

Results

Performance analysis showed that motor skills improved with practice, with the GPG outperforming the PPG in maze navigation efficiency. Biomechanical analysis revealed that the GPG demonstrated superior movement strategies, as indicated by higher peak velocities and fewer velocity peaks during task execution. Tactile feedback analysis showed that GPG participants applied more precise and focused pressure with their right-hand thumb, suggesting enhanced motor control. Cognitively, both groups refined their mental representation structures over time, but the GPG exhibited a more structured and sophisticated cognitive mapping of the task post-practice.

Discussion

The findings highlight the intertwined nature of biomechanical control, tactile feedback, and cognitive processing in motor skill acquisition. The results support established theories, such as the cognitive action architecture approach, emphasizing the role of mental representation in planning and executing motor actions. The integration of cognitive primitives in our analysis provides a theoretical framework that connects observable behaviors to underlying cognitive strategies, enhancing the understanding of motor learning across various contexts. Our study underscores the necessity of a holistic approach to motor learning research, recognizing the complex interaction between cognitive and motor processes in skill acquisition.

A computational study of how an α- to γ-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement

The primary motor cortex does not uniquely or directly produce alpha motoneurone (α-MN) drive to muscles during voluntary movement. Rather, α-MN drive emerges from the synthesis and competition among excitatory and inhibitory inputs from multiple descending tracts, spinal interneurons, sensory inputs, and proprioceptive afferents. One such fundamental input is velocity-dependent stretch reflexes in lengthening muscles, which should be inhibited to enable voluntary movement. It remains an open question, however, the extent to which unmodulated stretch reflexes disrupt voluntary movement, and whether and how they are inhibited in limbs with numerous multiarticular muscles. We used a computational model of a Rhesus Macaque arm to simulate movements with feedforward α-MN commands only, and with added velocity-dependent stretch reflex feedback. We found that velocity-dependent stretch reflex caused movement-specific, typically large and variable disruptions to arm movements. These disruptions were greatly reduced when modulating velocity-dependent stretch reflex feedback (i) as per the commonly proposed (but yet to be clarified) idealized alpha-gamma (α-γ) coactivation or (ii) an alternative α-MN collateral projection to homonymous γ-MNs. We conclude that such α-MN collaterals are a physiologically tenable propriospinal circuit in the mammalian fusimotor system. These collaterals could still collaborate with α-γ coactivation, and the few skeletofusimotor fibers (β-MNs) in mammals, to create a flexible fusimotor ecosystem to enable voluntary movement. By locally and automatically regulating the highly nonlinear neuro-musculo-skeletal mechanics of the limb, these collaterals could be a critical low-level enabler of learning, adaptation, and performance via higher-level brainstem, cerebellar, and cortical mechanisms.

Exploring motor skill acquisition in bimanual coordination: insights from navigating a novel maze task

In this study, we introduce a novel maze task designed to investigate naturalistic motor learning in bimanual coordination. We developed and validated an extended set of movement primitives tailored to capture the full spectrum of scenarios encountered in a maze game. Over a 3-day training period, we evaluated participants' performance using these primitives and a custom-developed software, enabling precise quantification of performance. Our methodology integrated the primitives with in-depth kinematic analyses and thorough thumb pressure assessments, charting the trajectory of participants' progression from novice to proficient stages. Results demonstrated consistent improvement in maze performance and significant adaptive changes in joint behaviors and strategic recalibrations in thumb pressure distribution. These findings highlight the central nervous system's adaptability in orchestrating sophisticated motor strategies and the crucial role of tactile feedback in precision tasks. The maze platform and setup emerge as a valuable foundation for future experiments, providing a tool for the exploration of motor learning and coordination dynamics. This research underscores the complexity of bimanual motor learning in naturalistic environments, enhancing our understanding of skill acquisition and task efficiency while emphasizing the necessity for further exploration and deeper investigation into these adaptive mechanisms.

The gated cascade diffusion model: An integrated theory of decision making, motor preparation, and motor execution

This article introduces an integrated and biologically inspired theory of decision making, motor preparation, and motor execution. The theory is formalized as an extension of the diffusion model, in which diffusive accumulated evidence from the decision-making process is continuously conveyed to motor areas of the brain that prepare the response, where it is smoothed by a mechanism that approximates a Kalman-Bucy filter. The resulting motor preparation variable is gated prior to reaching agonist muscles until it exceeds a particular level of activation. We tested this gated cascade diffusion model by continuously probing the electrical activity of the response agonists through electromyography in four choice tasks that span a variety of domains in cognitive sciences, namely motion perception, numerical cognition, recognition memory, and lexical knowledge. The model provided a good quantitative account of behavioral and electromyographic data and systematically outperformed previous models. This work represents an advance in the integration of processes involved in simple decisions and sheds new light on the interplay between decision and motor systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

BACKGROUND AND OBJECTIVE

A new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach.

METHODS

MMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies.

RESULTS

As an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License.

CONCLUSIONS

Thanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Terrestrial locomotion characteristics of climbing perch (Anabas testudineus)

The evolution and utilization of limbs facilitated terrestrial vertebrate movement on land, but little is known about how other lateral structures enhance terrestrial locomotion in amphibian fishes without terrestrialized limb structures. Climbing perch (Anabas testudineus) exhibit sustained terrestrial locomotion using uniaxial rotating gill covers instead of appendages. To investigate the role of such simple lateral structures in terrestrial locomotion and the motion-generating mechanism of the corresponding locomotor structure configuration (gill covers and body undulation), we measured the terrestrial kinematics of climbing perch and quantitatively analysed its motion characteristics. The digitized locomotor kinematics showed a unique body postural adjustment ability that enables the regulation of the posture of the caudal peduncle for converting lateral bending force into propulsion. An analysis of the coordination characteristics demonstrated that the motion of the gill cover is kinematically independent of axial undulation, suggesting that the gill cover functions as an anchored simple support pole while axial undulation actively mediates body posture and produces propulsive force. The two identified feature shapes explained more than 87% of the complex lateral undulation in multistage locomotion. The kinematic characteristics enhance our understanding of the underlying coordinating mechanism corresponding to locomotor configurations. Our work provides quantitative insight into the terrestrial locomotor adaptation of climbing perch and sheds light on terrestrial motion potential of locomotor configurations containing a typical aquatic body and restricted lateral structure.

Torque modulation mechanism of the knee joint during balance recovery

Exploring the torque modulation mechanisms of human joints is critical for analyzing the human balance control system and developing natural human-machine interactions for balance support. However, the knee joint is often overlooked in biomechanical models because of its limited range of motion during balance recovery. This poses a challenge in establishing mathematical models for the knee joint's torque modulation mechanisms using computer simulations based on the inverted pendulum model. This study aims to provide a simplified linear feedback model inspired by sensorimotor transformation theory to reveal the torque modulation mechanism of the knee joint. The model was validated using data from experiments involving support-surface translation perturbations. The goodness-of-fit metrics of the model, including R2 values and root mean square errors (RMSE), demonstrated strong explanatory power (R2 ranged from 0.77 to 0.90) and low error (RMSE ranging from 0.035 to 0.072) across different perturbation magnitudes and directions. Through pooling samples across various perturbation conditions and conducting multiple fits, this model revealed that knee torque is modulated using a direction-specific strategy with adaptable feedback gains. These results suggest that the proposed simplified linear model can be used to develop assistive systems and retrieve insights on balance recovery mechanisms.

Anthropomorphic motion planning for multi-degree-of-freedom arms

With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions.

Current perspectives on neuromodulation in ALS patients: A systematic review and meta-analysis

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, resulting in muscle weakness, paralysis, and eventually patient mortality. In recent years, neuromodulation techniques have emerged as promising potential therapeutic approaches to slow disease progression and improve the quality of life of ALS patients. A systematic review was conducted until August 8, 2023, to evaluate the neuromodulation methods used and their potential in the treatment of ALS. The search strategy was applied in the Cochrane Central database, incorporating results from other databases such as PubMed, Embase, CTgov, CINAHL, and ICTRP. Following the exclusion of papers that did not fulfil the inclusion criteria, a total of 2090 records were found, leaving a total of 10 studies. R software was used to conduct meta-analyses based on the effect sizes between the experimental and control groups. This revealed differences in muscle stretch measures with manual muscle testing (p = 0.012) and resting motor threshold (p = 0.0457), but not with voluntary isometric contraction (p = 0.1883). The functionality of ALS was also different (p = 0.007), but not the quality of life. Although intracortical facilitation was not seen in motor cortex 1 (M1) (p = 0.1338), short-interval intracortical inhibition of M1 was significant (p = 0.0001). BDNF showed no differences that were statistically significant (p = 0.2297). Neuromodulation-based treatments are proposed as a promising therapeutic approach for ALS that can produce effects on muscle function, spasticity, and intracortical connections through electrical, magnetic, and photonic stimulation. Photobiomodulation stands out as an innovative approach that uses specific wavelengths to influence mitochondria, with the aim of improving mitochondrial function and reducing excitotoxicity. The lack of reliable placebo controls and the variation in stimulation frequency are some of the drawbacks of neuromodulation.

Human arm redundancy: a new approach for the inverse kinematics problem

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Motor patterns of patients with spinal muscular atrophy suggestive of sensory and corticospinal contributions to the development of locomotor muscle synergies

Complex locomotor patterns are generated by combination of muscle synergies. How genetic processes, early sensorimotor experiences, and the developmental dynamics of neuronal circuits contribute to the expression of muscle synergies remains elusive. We shed light on the factors that influence development of muscle synergies by studying subjects with spinal muscular atrophy (SMA, types II/IIIa), a disorder associated with degeneration and deafferentation of motoneurons and possibly motor cortical and cerebellar abnormalities, from which the afflicted would have atypical sensorimotor histories around typical walking onset. Muscle synergies of children with SMA were identified from electromyographic signals recorded during active-assisted leg motions or walking, and compared with those of age-matched controls. We found that the earlier the SMA onset age, the more different the SMA synergies were from the normative. These alterations could not just be explained by the different degrees of uneven motoneuronal losses across muscles. The SMA-specific synergies had activations in muscles from multiple limb compartments, a finding reminiscent of the neonatal synergies of typically developing infants. Overall, while the synergies shared between SMA and control subjects may reflect components of a core modular infrastructure determined early in life, the SMA-specific synergies may be developmentally immature synergies that arise from inadequate activity-dependent interneuronal sculpting due to abnormal sensorimotor experience and other factors. Other mechanisms including SMA-induced intraspinal changes and altered cortical-spinal interactions may also contribute to synergy changes. Our interpretation highlights the roles of the sensory and descending systems to the typical and abnormal development of locomotor modules.NEW & NOTEWORTHY This is likely the first report of locomotor muscle synergies of children with spinal muscular atrophy (SMA), a subject group with atypical developmental sensorimotor experience. We found that the earlier the SMA onset age, the more the subjects' synergies deviated from those of age-matched controls. This result suggests contributions of the sensory/corticospinal activities to the typical expression of locomotor modules, and how their disruptions during a critical period of development may lead to abnormal motor modules.

Dynamic primitives in constrained action: systematic changes in the zero-force trajectory

Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge that gap, this study examined kinematically constrained motion as an intermediate step between the widely studied unconstrained motions and sparsely studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds (fast, medium, and very slow) as instructed via visual display. With the hand constrained to move in a circle, nonzero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to "subtract" peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion, a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction; that is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 s, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifesting as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.NEW & NOTEWORTHY Control using primitive dynamic actions may explain why human performance is superior to robots despite seemingly inferior "wetware"; however, this also implies limitations. For a crank-turning task, this work quantified two such informative limitations. Force was exerted even though it produced no mechanical work, the underlying zero-force trajectory was roughly elliptical, and its orientation differed with turning direction, evidence of oscillatory control. At slow speeds, speed variability increased substantially, indicating intermittent control via submovements.

A primitive-based representation of dance: modulations by experience and perceptual validity

The generation of complex movements such as dance might be possible due to the utilization of movement building blocks, i.e., movement primitives. However, it is largely unexplored how the temporal structure of a movement sequence and the recruitment of these primitives change with experience. Therefore, we obtained a representation of primitives with the temporal movement primitive model from the motion capture data of dancers with varying experiences, both for improvised and choreographed movements (elements from contemporary/modern/jazz) with different qualitative expressions. We analyzed differences between movement conditions regarding the number of temporal segments and the number of primitives, as well as their association with dance experience. Especially for the choreography with a neutral expression, the results indicate a negative association between experience and the number of segments and a positive association between experience and the number of primitives. The variation in the recruitment of these primitives suggests an increased consistency of modular control with experience, particularly for improvised dance. A prerequisite for the meaningful interpretation of these results regarding human movement production is that the model can generate perceptually valid dance movements. This was confirmed in a subsequent experiment, although the validity was slightly impaired for improvised movements. Overall, the results of the choreographed movement sequences suggest that experience is associated with an increase in motor repertoire that might facilitate fewer and longer temporal segments.NEW & NOTEWORTHY This study demonstrates that a temporal movement primitive model, trained with movements performed by dancers with different levels of experience, is able to generate natural-looking dance movements. The results suggest that motor experience in dance is associated not only with fewer temporal segments but also with an increase in the number of underlying movement building blocks. The recruitment of these primitives, which might be used to simplify movement production, additionally seems to become more consistent with experience.

Physics-based character animation and human motor control

Motor neuroscience and physics-based character animation (PBCA) approach human and humanoid control from different perspectives. The primary goal of PBCA is to control the movement of a ragdoll (humanoid or animal) applying forces and torques within a physical simulation. The primary goal of motor neuroscience is to understand the contribution of different parts of the nervous system to generate coordinated movements. We review the functional principles and the functional anatomy of human motor control and the main strategies used in PBCA. We then explore common research points by discussing the functional anatomy and ongoing debates in motor neuroscience from the perspective of PBCA. We also suggest there are several benefits to be found in studying sensorimotor integration and human-character coordination through closer collaboration between these two fields.

Gait asymmetry in children with Duchenne muscular dystrophy: evaluated through kinematic synergies and muscle synergies of lower limbs

BACKGROUND

Gait is a complex, whole-body movement that requires the coordinated action of multiple joints and muscles of our musculoskeletal system. In the context of Duchenne muscular dystrophy (DMD), a disease characterized by progressive muscle weakness and joint contractures, previous studies have generally assumed symmetrical behavior of the lower limbs during gait. However, such a symmetric gait pattern of DMD was controversial. One aspect of this is criticized, because most of these studies have primarily focused on univariate variables, rather than on the coordination of multiple body segments and even less investigate gait symmetry under a motor synergy of view.

METHODS

We investigated the gait pattern of 20 patients with DMD, compared to 18 typical developing children (TD) through 3D Gait Analysis. Kinematic and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. In addition, bilateral spatiotemporal variables of gait, such as stride length, percentage of stance and swing phase, step length, and percentage of double support phase, were used for calculating the symmetry index (SI) to evaluate gait symmetry as well.

RESULTS

Compared with the TD group, the DMD group walked with decreased gait velocity (both p < 0.01), stride length (both p < 0.01), and step length (both p < 0.001). No significant difference was found between groups in SI of all spatiotemporal parameters extracted between the left and right lower limbs. In addition, the DMD group exhibited lower kinematic synergy symmetry values compared to the TD group (p < 0.001), while no such significant group difference was observed in symmetry values of muscle synergy.

CONCLUSIONS

The findings of this study suggest that DMD influences, to some extent, the symmetry of synergistic movement of multiple segments of lower limbs, and thus kinematic synergy appears capable of discriminating gait asymmetry in children with DMD when conventional spatiotemporal parameters are unchanged.

Learning complex upper-limb movements through practicing movement elements

Upper-limb complex movements constitute a major part of our daily activities. Research shows complex movements are generated by sequences of movement elements represented by a unimodal bell-shaped velocity curve. We utilized this understanding in the field of motor skill acquisition and hypothesized that practicing a movement element of a complex movement trajectory will facilitate the performance on the respective complex movement trajectory. To test this, we designed an experiment where the control group learned a full complex trajectory, whereas the two elemental groups learned two different movement elements of the complex trajectory. The two main outcome measures explaining the performance were accuracy and speed. The elemental groups, after training on movement elements, significantly improved their speed and accuracy when tested on the full complex trajectory. The result illustrated that training on a movement element of a complex trajectory benefited the performance of the full complex trajectory. The two elemental groups showed similar improvements in the performance of the complex motor skill, despite obtaining training on different movement elements of the same complex movement. The findings show that complex movements can be learned by practicing their movement elements.

The Influence of Experience on Neuromuscular Control of the Body When Cutting at Different Angles

Cutting is an offensive technique commonly used in football and basketball to pass the opponent's defence by changing direction quickly in running. This paper aims to investigate the effect of experience and angle on the neuromuscular control strategies of the trunk and lower limbs during cutting. Non-negative matrix factorisation and K-means were used to extract muscle synergies (muscles that are activated in parallel) of 12 subjects with cut experience and 9 subjects without experience based on the sEMG signal collected from cutting at three cut angles (45°, 90°, and 135°), which was also mapped into the spinal motor output. Uncontrolled manifold analysis was used to establish the relationship between muscle synergies and COP. This study found that experienced subjects tended to use the lower limb muscles rather than the postural muscles as stabiliser muscles compared to novices. Experienced subjects can recruit an additional set of muscle synergy to cope with large-angle cuts. In addition, experienced subjects can activate the second muscle synergy, involving the hip and ankle stabilisation muscles, in advance to improve postural stability when cutting in large-angle. Synergy index of experienced subjects dropped rapidly before the quick stop and was relatively high during the change of direction. These results suggest that experience can modify the postural stabilisation mechanisms during cutting, and prompt the lower limb muscle synergy to produce anticipatory adjustment to improve postural stability in the anterior-posterior and internal-external directions.

Explaining human interactions on the road by large-scale integration of computational psychological theory

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

The neuromechanics of animal locomotion: From biology to robotics and back

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

EMG space similarity feedback promotes learning of expert-like muscle activation patterns in a complex motor skill

Augmented feedback provided by a coach or augmented reality system can facilitate the acquisition of a motor skill. Verbal instructions and visual aids can be effective in providing feedback about the kinematics of the desired movements. However, many skills require mastering not only kinematic, but also complex kinetic patterns, for which feedback is harder to convey. Here, we propose the electromyography (EMG) space similarity feedback, which may indirectly convey kinematic and kinetic feedback by comparing the muscle activations of the learner and an expert in the task. The EMG space similarity feedback is a score that reflects how well a set of muscle synergies extracted from the expert can reconstruct the learner's EMG when performing the task. We tested the EMG space similarity feedback in a virtual bimanual polishing task that uses a robotic system to simulate the dynamics of a real polishing operation. We measured the expert's and learner's EMG from eight muscles in each arm during the real and virtual polishing tasks, respectively. The goal of the virtual task was to smoothen the surface of a virtual object. Therefore, we defined performance in the task as the smoothness of the object at the end of a trial. We separated learners into real feedback and null feedback groups to assess the effects of the EMG space similarity feedback. The real and null feedback groups received veridic and no EMG space similarity feedback, respectively. Subjects participated in five training sessions on different days, and we evaluated their performance on each day. Subjects in both groups were able to increase smoothness throughout the training sessions, with no significant differences between groups. However, subjects in the real feedback group were able to improve in the EMG space similarity score to a significantly greater extent than the null feedback group. Additionally, subjects in the real feedback group produced muscle activations that became increasingly consistent with an important muscle synergy found in the expert. Our results indicate that the EMG space similarity feedback promotes acquiring expert-like muscle activation patterns, suggesting that it may assist in the acquisition of complex motor skills.

Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation

Understanding how movement is controlled by the CNS remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons and muscle synergies are usually addressed separately and therefore seen as independent features of motor control. In this review, we analyse the body of literature in a broader perspective and we identify a unified approach to explain apparently divergent observations at different scales of motor control. Specifically, we propose a new conceptual framework of the neural control of movement, which merges the concept of common input to motor neurons and modular control, together with the constraints imposed by recruitment order. This framework is based on the following assumptions: (1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; (2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; (3) clusters represent functional modules used by the CNS to reduce the dimensionality of the control; and (4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. Here, we discuss this framework and its underlying theoretical and experimental evidence.

Modular organization of locomotor networks in people with severe spinal cord injury

Introduction

Previous studies support modular organization of locomotor circuitry contributing to the activation of muscles in a spatially and temporally organized manner during locomotion. Human spinal circuitry may reorganize after spinal cord injury; however, it is unclear if reorganization of spinal circuitry post-injury affects the modular organization. Here we characterize the modular synergy organization of locomotor muscle activity expressed during assisted stepping in subjects with complete and incomplete spinal cord injury (SCI) of varying chronicity, before any explicit training regimen. We also investigated whether the synergy characteristics changed in two subjects who achieved independent walking after training with spinal cord epidural stimulation.

Methods

To capture synergy structures during stepping, individuals with SCI were stepped on a body-weight supported treadmill with manual facilitation, while electromyography (EMGs) were recorded from bilateral leg muscles. EMGs were analyzed using non-negative matrix factorization (NMF) and independent component analysis (ICA) to identify synergy patterns. Synergy patterns from the SCI subjects were compared across different clinical characteristics and to non-disabled subjects (NDs).

Results

Results for both NMF and ICA indicated that the subjects with SCI were similar among themselves, but expressed a greater variability in the number of synergies for criterion variance capture compared to NDs, and weaker correlation to NDs. ICA yielded a greater number of muscle synergies than NMF. Further, the clinical characteristics of SCI subjects and chronicity did not predict any significant differences in the spatial synergy structures despite any neuroplastic changes. Further, post-training synergies did not become closer to ND synergies in two individuals.

Discussion

These findings suggest fundamental differences between motor modules expressed in SCIs and NDs, as well as a striking level of spatial and temporal synergy stability in motor modules in the SCI population, absent the application of specific interventions.

Learning-induced changes in the neural circuits underlying motor sequence execution

As the old adage goes: practice makes perfect. Yet, the neural mechanisms by which rote repetition transforms a halting behavior into a fluid, effortless, and "automatic" action are not well understood. Here we consider the possibility that well-practiced motor sequences, which initially rely on higher-level decision-making circuits, become wholly specified in lower-level control circuits. We review studies informing this idea, discuss the constraints on such shift in control, and suggest approaches to pinpoint circuit-level changes associated with motor sequence learning.

Toward a unifying framework for the modeling and identification of motor primitives

A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules-named motor primitives-that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models.

Age-related differences in gait symmetry obtained from kinematic synergies and muscle synergies of lower limbs during childhood

The age-related changes of gait symmetry in healthy children concerning individual joint and muscle activation data have previously been widely studied. Extending beyond individual joints or muscles, identifying age-related changes in the coordination of multiple joints or muscles (i.e., muscle synergies and kinematic synergies) could capture more closely the underlying mechanisms responsible for gait symmetry development. To evaluate the effect of age on the symmetry of the coordination of multiple joints or muscles during childhood, we measured gait symmetry by kinematic and EMG data in 39 healthy children from 2 years old to 14 years old, divided into three equal age groups: preschool children (G1; 2.0-5.9 years), children (G2; 6.0-9.9 years), pubertal children (G3; 10.0-13.9 years). Participants walked barefoot at a self-selected walking speed during three-dimensional gait analysis (3DGA). Kinematic synergies and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. Statistical analysis was performed to examine intergroup differences. The results showed that the effect of age was significant on the symmetry values extracted by kinematic synergies, while older children exhibited higher kinematic synergy symmetry values compared to the younger group. However, no significant age-related changes in symmetry values of muscle synergy were observed. It is suggested that kinematic synergy of lower joints can be asymmetric at the onset of independent walking and showed improving symmetry with increasing age, whereas the age-related effect on the symmetry of muscle synergies was not demonstrated. These data provide an age-related framework and normative dataset to distinguish age-related differences from pathology in children with neuromotor disorders.

Relationship between rate-elicited changes in muscular-kinematic control strategies and acoustic performance in individuals with ALS-A multimodal investigation

INTRODUCTION

As a key control variable, duration has been long suspected to mediate the organization of speech motor control strategies, which has management implications for neuromotor speech disorders. This study aimed to experimentally delineate the role of duration in organizing speech motor control in neurologically healthy and impaired speakers using a voluntary speaking rate manipulation paradigm.

METHODS

Thirteen individuals with amyotrophic lateral sclerosis (ALS) and 10 healthy controls performed a sentence reading task three times, first at their habitual rate, then at a slower rate. A multimodal approach combining surface electromyography, kinematic, and acoustic technologies was used to record jaw muscle activities, jaw kinematics, and speech acoustics. Six muscular-kinematic features were extracted and factor-analyzed to characterize the organization of the mandibular control hierarchy. Five acoustic features were extracted, measuring the spectrotemporal properties of the diphthong /ɑɪ/ and the plosives /t/ and /k/.

RESULTS

The muscular-kinematic features converged into two interpretable latent factors, reflecting the level and cohesiveness/flexibility of mandibular control, respectively. Voluntary rate reduction led to a trend toward (1) finer, less cohesive, and more flexible mandibular control, and (2) increased range and decreased transition slope of the diphthong formants, across neurologically healthy and impaired groups. Differential correlations were found between the rate-elicited changes in mandibular control and acoustic performance for neurologically healthy and impaired speakers.

CONCLUSIONS

The results provided empirical evidence for the long-suspected but previously unsubstantiated role of duration in (re)organizing speech motor control strategies. The rate-elicited reorganization of muscular-kinematic control contributed to the acoustic performance of healthy speakers, in ways consistent with theoretical predictions. Such contributions were less consistent in impaired speakers, implying the complex nature of speaking rate reduction in ALS, possibly reflecting an interplay of disease-related constraints and volitional duration control. This information may help to stratify and identify candidates for the rate manipulation therapy.

Poststroke arm and hand paresis: should we target the cervical spinal cord?

Despite advances in understanding of corticospinal motor control and stroke pathophysiology, current rehabilitation therapies for poststroke upper limb paresis have limited efficacy at the level of impairment. To address this problem, we make the conceptual case for a new treatment approach. We first summarize current understanding of motor control deficits in the arm and hand after stroke and their shared physiological mechanisms with spinal cord injury (SCI). We then review studies of spinal cord stimulation (SCS) for recovery of locomotion after SCI, which provide convincing evidence for enhancement of residual corticospinal function. By extrapolation, we argue for using cervical SCS to restore upper limb motor control after stroke.

Epidural electrical stimulation of the cervical dorsal roots restores voluntary upper limb control in paralyzed monkeys

Regaining arm control is a top priority for people with paralysis. Unfortunately, the complexity of the neural mechanisms underlying arm control has limited the effectiveness of neurotechnology approaches. Here, we exploited the neural function of surviving spinal circuits to restore voluntary arm and hand control in three monkeys with spinal cord injury, using spinal cord stimulation. Our neural interface leverages the functional organization of the dorsal roots to convey artificial excitation via electrical stimulation to relevant spinal segments at appropriate movement phases. Stimulation bursts targeting specific spinal segments produced sustained arm movements, enabling monkeys with arm paralysis to perform an unconstrained reach-and-grasp task. Stimulation specifically improved strength, task performances and movement quality. Electrophysiology suggested that residual descending inputs were necessary to produce coordinated movements. The efficacy and reliability of our approach hold realistic promises of clinical translation.

Muscle activity in explicit and implicit sequence learning: Exploring additional measures of learning and certainty via tensor decomposition

Sequence learning in serial reaction time tasks (SRTTs) is usually inferred through the reaction time measured by a keyboard. However, this chronometric parameter offers no information beyond the time point of the button-press. We therefore examined whether sequence learning can be measured by muscle activations via electromyography (EMG) in a dual-task paradigm. The primary task was a SRTT, in which the stimuli followed a fixed sequence in some blocks, whereas the sequence was random in the control condition. The secondary task stimulus was always random. One group was informed about the fixed sequence, and the other not. We assessed three dependent variables. The chronometric parameter premotor time represents the duration between stimulus onset and the onset of EMG activity, which indicates the start of the response. The other variables describe the response itself considering the EMG activity after response start. The EMG integral was analyzed, and additionally, tensor decomposition was implemented to assess sequence dependent changes in the contribution of the obtained subcomponents. The results show explicit sequence learning in this dual-task setting. Specifically, the informed group show shorter premotor times in fixed than random sequences as well as larger EMG integral and tensor contributions. Further, increased activity seems to represent response certainty, since a decrease is found for both groups in trials following erroneous responses. Interestingly, the sensitivity to sequence and post-error effects varies between the subcomponents. The results indicate that muscle activity can be a useful indicator of response behavior in addition to chronometric parameters.

Central Commands to the Elbow and Shoulder Muscles During Circular Planar Movements of Hand With Simultaneous Generation of Tangential Forces

This study examines some of the non-linear effects of signal transduction in the human motor system, with particular emphasis on muscle hysteresis. The movement tests were analyzed in a group of eight subjects, which were asked to develop tangential force using visual biofeedback while performing slow, externally imposed, circular movements of right hand holding a moving handle operated by a computerized mechatronic system. The positional changes in the averaged EMGs of the elbow and shoulder muscles were compared for all combinations of direction of movement and generated force. Additionally, for one of the subjects, there was carried out MRI identification and 3D printing of the bones of the forelimb, shoulder, scapula and collarbone, which made it possible to reconstruct for him the length and force traces of all the muscles under study. The averaged EMG traces in muscles of both joints show their close correspondence to the related force traces, however, the co-activation patterns of activity in agonists and antagonists were also often encountered. The EMG waves related to the respective force waves were strongly dependent on the predominant direction of the muscle length changes within the correspondent force wave locations: the EMG intensities were higher for the shortening muscle movements (concentric contractions) and lower during muscle lengthening (eccentric contractions). The data obtained allows to suggest that for two-joint movements of the forelimbs, it is sufficient to consider the force and activation synergies (patterns of simultaneous activity in different muscles), ignoring at the first stage the effects associated with kinematic synergy. On the other hand, the data obtained indicate that the movement kinematics has a strong modulating effect on the activation synergy, dividing it into concentric and eccentric subtypes, in accordance with the known non-linear features of the muscle dynamics. It has been shown that the concentric and eccentric differences in the responses of the shoulder muscles are more clearly distinguishable than those in the elbow muscles. The shoulder muscles also have a more pronounced symmetry of the averaged EMG responses with respect to the ascending and descending phases of force waves, while demonstrating a lower degree of antagonist cocontraction. The data obtained suggest that the central commands in two-joint movements are determined mainly by the interdependence of force and activation synergies including both intra- and inter-joint components, while kinematic synergy can be interpreted as a potent modulator of activation synergy.

Task space exploration improves adaptation after incompatible virtual surgeries

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns, but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies, required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study, but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than after compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time, can learn new muscle synergies.

Neural Network Models for Spinal Implementation of Muscle Synergies

Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control; however, their neural implementation is still unclear. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary “modules” in the spinal cord. Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies.

Latent characteristics and neural manifold of brain functional network under acupuncture

Acupuncture can regulate the cognition of brain system, and different manipulations are the keys of realizing the curative effect of acupuncture on human body. Therefore, it is crucial to distinguish and monitor the different acupuncture manipulations automatically. In this brief, in order to enhance the robustness of electroencephalogram (EEG) detection against noise and interference, we propose an acupuncture manipulation detecting framework based on supervised ISOMAP and recurrent neural network (RNN). Primarily, the low-dimensional embedding neural manifold of brain dynamical functional network is extracted via the reconstructed geodetic distance. It is found that there exhibits stronger acupuncture-specific reconfiguration of brain network. Besides, we show that the distance travel along this manifold correlates strongly with changes of acupuncture manipulations. The low-dimensional brain topological structure of all subjects shows crescent-like feature when acupuncturing at Zusanli acupoints, and fixed-points are varying under diverse manipulation methods. Moreover, Takagi-Sugeno-Kang (TSK) classifier is adopted to identify acupuncture manipulations according to the nonlinear characteristics of neural manifolds. Compared with different classifier, TSK can further improve the accuracy of manipulation identification at 96.71%. The results demonstrate the effectiveness of our model in detecting the acupuncture manipulations, which may provide neural biomarkers for acupuncture physicians.

Evidence for an internal model of friction when controlling kinetic energy at impact to slide an object along a surface toward a target

Although the role of an internal model of gravity for the predictive control of the upper limbs is quite well established, evidence is lacking regarding an internal model of friction. In this study, 33 male and female human participants performed a striking movement (with the index finger) to slide a plastic cube-like object to a given target distance. The surface material (aluminum or balsa wood) on which the object slides, the surface slope (-10°, 0, or +10°) and the target distance (25 cm or 50 cm) varied across conditions, with ten successive trials in each condition. Analysis of the object speed at impact and spatial error suggests that: 1) the participants chose to impart a similar speed to the object in the first trial regardless of the surface material to facilitate the estimation of the coefficient of friction; 2) the movement is parameterized across repetitions to reduce spatial error; 3) an internal model of friction can be generalized when the slope changes. Biomechanical analysis showed interindividual variability in the recruitment of the upper limb segments and in the adjustment of finger speed at impact in order to transmit the kinetic energy required to slide the object to the target distance. In short, we provide evidence that the brain builds an internal model of friction that makes it possible to parametrically control a striking movement in order to regulate the amount of kinetic energy required to impart the appropriate initial speed to the object.

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.

Characterization of stroke-related upper limb motor impairments across various upper limb activities by use of kinematic core set measures

Background

Upper limb kinematic assessments provide quantifiable information on qualitative movement behavior and limitations after stroke. A comprehensive characterization of spatiotemporal kinematics of stroke subjects during upper limb daily living activities is lacking. Herein, kinematic expressions were investigated with respect to different movement types and impairment levels for the entire task as well as for motion subphases.

Method

Chronic stroke subjects with upper limb movement impairments and healthy subjects performed a set of daily living activities including gesture and grasp movements. Kinematic measures of trunk displacement, shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension, forearm pronation/supination, wrist flexion/extension, movement time, hand peak velocity, number of velocity peaks (NVP), and spectral arc length (SPARC) were extracted for the whole movement as well as the subphases of reaching distally and proximally. The effects of the factors gesture versus grasp movements, and the impairment level on the kinematics of the whole task were tested. Similarities considering the metrics expressions and relations were investigated for the subphases of reaching proximally and distally between tasks and subgroups.

Results

Data of 26 stroke and 5 healthy subjects were included. Gesture and grasp movements were differently expressed across subjects. Gestures were performed with larger shoulder motions besides higher peak velocity. Grasp movements were expressed by larger trunk, forearm, and wrist motions. Trunk displacement, movement time, and NVP increased and shoulder flexion/extension decreased significantly with increased impairment level. Across tasks, phases of reaching distally were comparable in terms of trunk displacement, shoulder motions and peak velocity, while reaching proximally showed comparable expressions in trunk motions. Consistent metric relations during reaching distally were found between shoulder flexion/extension, elbow flexion/extension, peak velocity, and between movement time, NVP, and SPARC. Reaching proximally revealed reproducible correlations between forearm pronation/supination and wrist flexion/extension, movement time and NVP.

Conclusion

Spatiotemporal differences between gestures versus grasp movements and between different impairment levels were confirmed. The consistencies of metric expressions during movement subphases across tasks can be useful for linking kinematic assessment standards and daily living measures in future research and performing task and study comparisons.

Trial registration: ClinicalTrials.gov Identifier NCT03135093. Registered 26 April 2017, https://clinicaltrials.gov/ct2/show/NCT03135093.

The effect of limb position on a static knee extension task can be explained with a simple spinal cord circuit model

The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. We performed an isometric knee extension task experiment based on two common clinical tests for mobility and flexibility. The task was carried out at four preset angles of the knee, and we recorded from five muscles for two different hip positions. We applied muscle synergy analysis using nonnegative matrix factorization on surface electromyograph recordings to identify patterns in the data that changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesized that such patterns arise from the way proprioceptive and cortical signals are integrated in neural circuits of the spinal cord. Using the MIIND neural simulation platform, we developed a computational model based on current understanding of spinal circuits with an adjustable afferent input. The model produces the same synergy trends as observed in the data, driven by changes in the afferent input. To match the activation patterns from each knee angle and position of the experiment, the model predicts the need for three distinct inputs: two to control a nonlinear bias toward the extensors and against the flexors, and a further input to control additional inhibition of rectus femoris. The results show that proprioception may be involved in modulating muscle synergies encoded in cortical or spinal neural circuits.NEW & NOTEWORTHY The role of sensory feedback in motor control when limbs are held in a fixed position is disputed. We performed a novel experiment involving fixed position tasks based on two common clinical tests. We identified patterns of muscle activity during the tasks that changed with different leg positions and then inferred how sensory feedback might influence the observations. We developed a computational model that required three distinct inputs to reproduce the activity patterns observed experimentally. The model provides a neural explanation for how the activity patterns can be changed by sensory feedback.