Simultaneous control of natural and extra degrees of freedom by isometric force and electromyographic activity in the muscle-to-force null space

Electro-tactile modulation of muscle activation and intermuscular coordination in the human upper extremity

Electro-tactile stimulation (ETS) can be a promising aid in augmenting sensation for those with sensory deficits. Although applications of ETS have been explored, the impact of ETS on the underlying strategies of neuromuscular coordination remains largely unexplored. We investigated how ETS, alone or in the presence of mechano-tactile environment change, modulated the electromyogram (EMG) of individual muscles during force control and how the stimulation modulated the attributes of intermuscular coordination, assessed by muscle synergy analysis, in human upper extremities. ETS was applied to either the thumb or middle fingertip which had greater contact with the handle, grasped by the participant, and supported a target force match. EMGs were recorded from 11 arm muscles of 15 healthy participants during three-dimensional exploratory force control. EMGs were modeled as the linear combination of muscle co-activation patterns (the composition of muscle synergies) and their activation profiles. Individual arm muscle activation changed depending on the ETS location on the finger. The composition of muscle synergies was conserved, but synergy activation coefficients altered reflecting the effects of electro-tactile modulation. The mechano-tactile modulation tended to decrease the effects of ETS on the individual muscle activation and synergy activation magnitude. This study provides insights into sensory augmentation and its impact on intermuscular coordination in the human upper extremity.

Control limitations in the null-space of the wrist muscle system

The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmentation, where the set of muscle activations that do not produce force/torque (muscle-to-force null-space) could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed (i) if controlled variation of the muscle activation patterns in the wrist joint's null-space is possible; and (ii) whether force and null-space cursor targets could be reached concurrently. During the null-space target reaching condition, participants used muscle-to-force null-space muscle activation to move their cursor towards a displayed target while minimising the exerted force as visualised through the cursor's size. Initial targets were positioned to require natural co-contraction in the null-space and if participants showed a consistent ability to reach for their current target, they would rotate 5∘ incrementally to generate muscle activation patterns further away from their natural co-contraction. In contrast, during the concurrent target reaching condition participants were required to match a target position and size, where their cursor position was instead controlled by their exerted flexion-extension and radial-ulnar deviation, while its size was changed by their natural co-contraction magnitude. The results collected from 10 participants suggest that while there was variation in each participant's co-contraction behaviour, most did not possess the ability to control this variation for muscle-to-force null-space virtual reaching. In contrast, participants did show a direction and target size dependent ability to vary isometric force and co-contraction activity concurrently. Our results indicate the limitations of using the muscle-to-force null-space activity of joints with a low level of redundancy as a possible command signal for movement augmentation.

Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics

The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.

Human motor augmentation with an extra robotic arm without functional interference

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality-based testing and training and the proposed pipeline.

Beyond simple laboratory studies: Developing sophisticated models to study rich behavior

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

Exposure to an incompatible virtual surgery impacts the null space components of the muscle patterns after re-adaptation but not the task performance

Under the synergy hypothesis, novel muscle synergies may be required for motor skill learning. We have developed a "virtual surgery" experimental paradigm that alters the mapping of muscle activations onto virtual cursor motion during an isometric reaching task using myoelectric control. By creating virtual surgeries that are "incompatible" with the original synergies, we can investigate learning new muscle synergies in controlled experimental conditions. We have previously shown that participants are able to improve their task performance after an incompatible virtual surgery, using novel muscle patterns to overcome the perturbation. In this work, we investigated whether the activation of novel muscle patterns, that are required after an incompatible virtual surgery, affects task performance or the muscle patterns after re-adaptation to the unperturbed baseline mapping. We found that experiencing an incompatible virtual surgery did not affect the task performance during the baseline mapping. However, the adaptation to the incompatible virtual surgery resulted in changes in the null space components of the muscle patterns used in the unperturbed task.

Muscle synergies for evaluating upper limb in clinical applications: A systematic review

INTRODUCTION

Muscle synergies have been proposed as a strategy employed by the central nervous system to control movements. Muscle synergy analysis is a well-established framework to examine the pathophysiological basis of neurological diseases and has been applied for analysis and assessment in clinical applications in the last decades, even if it has not yet been widely used in clinical diagnosis, rehabilitative treatment and interventions. Even if inconsistencies in the outputs among studies and lack of a normative pipeline including signal processing and synergy analysis limit the progress, common findings and results are identifiable as a basis for future research. Therefore, a literature review that summarizes methods and main findings of previous works on upper limb muscle synergies in clinical environment is needed to i) summarize the main findings so far, ii) highlight the barriers limiting their use in clinical applications, and iii) suggest future research directions needed for facilitating translation of experimental research to clinical scenarios.

METHODS

Articles in which muscle synergies were used to analyze and assess upper limb function in neurological impairments were reviewed. The literature research was conducted in Scopus, PubMed, and Web of Science. Experimental protocols (e.g., the aim of the study, number and type of participants, number and type of muscles, and tasks), methods (e.g., muscle synergy models and synergy extraction methods, signal processing methods), and the main findings of eligible studies were reported and discussed.

RESULTS

383 articles were screened and 51 were selected, which involved a total of 13 diseases and 748 patients and 1155 participants. Each study investigated on average 15 ± 10 patients. Four to forty-one muscles were included in the muscle synergy analysis. Point-to-point reaching was the most used task. The preprocessing of EMG signals and algorithms for synergy extraction varied among studies, and non-negative matrix factorization was the most used method. Five EMG normalization methods and five methods for identifying the optimal number of synergies were used in the selected papers. Most of the studies report that analyses on synergy number, structure, and activations provide novel insights on the physiopathology of motor control that cannot be gained with standard clinical assessments, and suggest that muscle synergies may be useful to personalize therapies and to develop new therapeutic strategies. However, in the selected studies synergies were used only for assessment; different testing procedures were used and, in general, study-specific modifications of muscle synergies were observed; single session or longitudinal studies mainly aimed at assessing stroke (71% of the studies), even though other pathologies were also investigated. Synergy modifications were either study-specific or were not observed, with few analyses available for temporal coefficients. Thus, several barriers prevent wider adoption of muscle synergy analysis including a lack of standardized experimental protocols, signal processing procedures, and synergy extraction methods. A compromise in the design of the studies must be found to combine the systematicity of motor control studies and the feasibility of clinical studies. There are however several potential developments that might promote the use of muscle synergy analysis in clinical practice, including refined assessments based on synergistic approaches not allowed by other methods and the availability of novel models. Finally, neural substrates of muscle synergies are discussed, and possible future research directions are proposed.

CONCLUSIONS

This review provides new perspectives about the challenges and open issues that need to be addressed in future work to achieve a better understanding of motor impairments and rehabilitative therapy using muscle synergies. These include the application of the methods on wider scales, standardization of procedures, inclusion of synergies in the clinical decisional process, assessment of temporal coefficients and temporal-based models, extensive work on the algorithms and understanding of the physio-pathological mechanisms of pathology, as well as the application and adaptation of synergy-based approaches to various rehabilitative scenarios for increasing the available evidence.

Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches

Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R2 = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R2 = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.

Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System

In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The "virtual stiffness" method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations.

Principles of human movement augmentation and the challenges in making it a reality

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.