Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements

Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

International audienceVoluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements

Neural and motor basis of inter-individual interactions

The goal of my Ph.D. work was to investigate the behavioral markers and the brain activities responsible for the emergence of sensorimotor communication. Sensorimotor communication can be defined as a form of communication consisting into flexible exchanges based on bodily signals, in order to increase the efficiency of the inter-individual coordination. For instance, a soccer player carving his movements to inform another player about his intention. This form of interaction is highly dependent of the motor system and the ability to produce appropriate movements but also of the ability of the partner to decode these cues. To tackle these facets of human social interaction, we approached the complexity of the problem by splitting my research activities into two separate lines of research. First, we pursued the examination of motor-based humans\u2019 capability to perceive and \u201cread\u201d other\u2019s behaviors in focusing on single-subject experiment. The discovery of mirror neurons in monkey premotor cortex in the early nineties (di Pellegrino et al. 1992) motivated a number of human studies on this topic (Rizzolatti and Craighero 2004). The critical finding was that some ventral premotor neurons are engaged during visual presentation of actions performed by conspecifics. More importantly, those neurons were shown to encode also the actual execution of similar actions (i.e. irrespective of who the acting individual is). This phenomenon has been highly investigated in humans by using cortical and cortico-spinal measures (for review see, fMRI: Molenberghs, Cunnington, and Mattingley 2012; TMS: Naish et al. 2014; EEG: Pineda 2008). During single pulse TMS (over the primary motor cortex), the amplitude of motor evoked potentials (MEPs) provides an index of corticospinal recruitment. During action observation the modulation of this index follow the expected changes during action execution (Fadiga et al. 1995). However, dozens of studies have been published on this topic and revealed important inconsistencies. For instance, MEPs has been shown to be dependent on observed low-level motor features (e.g. kinematic features or electromyography temporal coupling; Gangitano, Mottaghy, and Pascual-Leone 2001; Borroni et al. 2005; Cavallo et al. 2012) as well as high level movement properties (e.g. action goals; Cattaneo et al. 2009; Cattaneo et al. 2013). Furthermore, MEPs modulations do not seem to be related to the observed effectors (Borroni and Baldissera 2008; Finisguerra et al. 2015; Senna, Bolognini, and Maravita 2014), suggesting their independence from low-level movement features. These contradictions call for new paradigms. Our starting hypothesis here is that the organization and function of the mirror mechanism should follow that of the motor system during action execution. Hence, we derived three action observation protocols from classical motor control theories: 1) The first study was motivated by the fact that motor redundancy in action execution do not allow the presence of a one-to-one mapping between (single) muscle activation and action goals. Based on that, we showed that the effect of action observation (observation of an actor performing a power versus a precision grasp) are variable at the single muscle level (MEPs; motor evoked potentials) but robust when evaluating the kinematic of TMS-evoked movements. Considering that movements are based on the coordination of multiple muscle activations (muscular synergies), MEPs may represent a partial picture of the real corticospinal activation. Inversely, movement kinematics is both the final functional byproduct of muscles coordination and the sole visual feedback that can be extracted from action observation (i.e. muscle recruitment is not visible). We conclude that TMS-evoked kinematics may be more reliable in representing the state of the motor system during action observation. 2) In the second study, we exploited the inter-subject variability inherent to everyday whole-body human actions, to evaluate the link between individual motor signatures (or motor styles) and other\u2019s action perception. We showed no group-level effect but a robust correlation between the individual motor signature recorded during action execution and the subsequent modulations of corticospinal excitability during action observation. However, results were at odds with a strict version of the direct matching hypothesis that would suggest the opposite pattern. In fact, the more the actor\u2019s movement was similar to the observer\u2019s individual motor signature, the smaller was the MEPs amplitude, and vice versa. These results conform to the predictive coding hypothesis, suggesting that during AO, the motor system compares our own way of doing the action (individual motor signature) with the action displayed on the screen (actor\u2019s movement). 3) In the third study, we investigated the neural mechanisms underlying the visual perception of action mistakes. According to a strict version of the direct matching hypothesis, the observer should potentially reproduce the neural activation present during the actual execution of action errors (van Schie et al. 2004). Here, instead of observing an increase of cortical inhibition, we showed an early (120 ms) decrease of intracortical inhibition (short intracortical inhibition) when a mismatch was present between the observed action (erroneous) and the observer\u2019s expectation. As proposed by the predictive coding framework, the motor system may be involved in the generation of an error signal potentially relying on an early decrease of intracortical inhibition within the corticomotor system. The second line of research aimed at the investigation of how sensorimotor communication flows between agents engaged in a complementary action coordination task. In this regard, measures of interest where related to muscle activity and/or kinematics as the recording of TMS-related indexes would be too complicated in a joint-action scenario. 1) In the first study, we exploited the known phenomenon of Anticipatory Postural Adjustments (APAs). APAs refers to postural adjustments made in anticipation of a self- or externally-generated disturbance in order to cope for the predicted perturbation and stabilize the current posture. Here we examined how observing someone else lifting an object we hold can affect our own anticipatory postural adjustments of the arm. We showed that the visual information alone (joint action condition), in the absence of efference copy (present only when the subject is unloading by himself the object situated on his hand), were not sufficient to fully deploy the needed anticipatory muscular activations. Rather, action observation elicited a dampened APA response that is later augmented by the arrival of tactile congruent feedback. 2) In a second study, we recorded the kinematic of orchestra musicians (one conductor and two lines of violinists). A manipulation was added to perturb the normal flow of information conveyed by the visual channel. The first line of violinist where rotated 180\ub0, and thus faced the second line. Several techniques were used to extract inter-group (Granger Causality method) and intra-group synchronization (PCA for musicians and autoregression for conductors). The analyses were directed to two kinematic features, hand and head movements, which are central for functionally different action. The hand is essential for instrumental actions, whereas head movements encode ancillary expressive actions. During the perturbation, we observed a complete reshaping of the whole patterns of communication going in the direction of a distribution of the leadership between conductor and violinists, especially for what regards head movements. In fact, in the perturbed condition, the second line acts as an informational hub connecting the first line to the conductor they no longer can see. This study evidences different forms of communications (coordination versus synchronization) flowing via different channels (ancillary versus instrumental) with different time-scales

Robust muscle synergies for postural control

The musculoskeletal structure of the human and animal body provides multiple solutions for performing any single motor behavior. The long-term goal of the work presented here is to determine the neuromechanical strategies used by the nervous system to appropriately coordinate muscles in order to achieve the performance of daily motor tasks. The overall hypothesis is that the nervous system simplifies muscle coordination by the flexible activation of muscle synergies, defined as a group of muscles activated as a unit, that perform task-level biomechanical functions. To test this hypothesis we investigated whether muscle synergies can be robustly used as building blocks for constructing the spatiotemporal muscle coordination patterns in human and feline postural control under a variety of biomechanical contexts. We demonstrated the generality and robustness of muscle synergies as a simplification strategy for both human and animal postural control. A few robust muscle synergies were able to reproduce the spatial and temporal variability in human and cat postural responses, regardless of stance configuration and perturbation type. In addition inter-trial variability in human postural responses was also accounted for by these muscle synergies. Finally, the activation of each muscle synergy in cat produced a specific stabilizing force vector, suggesting that muscle synergies control task-level variables. The identified muscle synergies may represent general modules of motor output underlying muscle coordination in posture that can be activated in different sensory contexts to achieve different postural goals. Therefore muscle synergies represents a simplifying mechanism for muscle coordination in natural behaviors not only because it is a strategy for reducing the number of variables to be controlled, but because it represents a mechanism for simply controlling multi-segmental task-level variables.Ph.D.Committee Chair: Ting, Lena H.; Committee Member: Chang, Young-Hui; Committee Member: Lee, Robert H.; Committee Member: Nichols, T. Richard; Committee Member: Wolf, Steve L

A Human Motor Control Framework based on Muscle Synergies

In spite of the complexities of the human musculoskeletal system, the central nervous system has the ability to orchestrate difficult motor tasks. Many researchers have tried to understand how the human nervous system works. Yet, our knowledge about the integration of sensory information and motor control is incomplete. This thesis presents a mathematical motor control framework that is developed to give the scientific community a biologically-plausible feedback controller for fast and efficient control of musculoskeletal systems. This motor control framework can be applied to musculoskeletal systems of various complexities, which makes it a viable tool for many predictive musculoskeletal simulations, assistive device design and control, and general motor control studies. The most important feature of this real-time motor control framework is its emphasis on the intended task. In this framework, a task is distinguished by the kinematic variables that need to be controlled. For example, in a reaching task, the task variables are the position of the hand (individual joint angles are irrelevant to the reaching task). Consequently, the task space is defined as the subspace that is formed by all the controlled variables. This motor control framework employs a hierarchical structure to speed up the calculations while maintaining high control efficiency. In this framework, there is a high-level controller, which deals with path planning and error compensation in the task space. The output of this task space controller is the acceleration vector in the task space, which needs to be fulfilled by muscle activities. The fast and efficient transformation of the task space accelerations to muscle activities in real-time is a main contribution of this research. Instead of using optimization to solve for the muscle activations (the usual practice in the past), this acceleration-to-activation (A2A) mapping uses muscle synergies to keep the computations simple enough to be real-time implementable. This A2A mapping takes advantage of the known effect of muscle synergies in the task space, thereby reducing the optimization problem to a vector decomposition problem. To make the result of the A2A mapping more efficient, the novel concept of posture-dependent synergies is introduced. The validity of the assumptions and the performance of the motor control framework are assessed using experimental trials. The experimental results show that the motor control framework can reconstruct the measured muscle activities only using the task-related kinematic/dynamic information. The application of the motor control framework to feedback motion control of musculoskeletal systems is also presented in this thesis. The framework is applied to musculoskeletal systems of various complexities (up to four-degree-of-freedom systems with 15 muscles) to show its effectiveness and generalizability to different dimensions. The control of functional electrical stimulation (FES) is another important application of my motor control framework. In FES, the muscles are activated by external electrical pulses to generate force, and consequently motion in paralysed limbs. There exists no feedback FES controller of upper extremity movements in the literature. The proposed motor control model is the first feedback FES controller that can be used for the control of reaching movements to arbitrary targets. Experimental results show that the motor control model is fast enough and accurate enough to be used as a practical motion controller for FES systems. Using such a biologically-plausible motor control model, it is possible to control the motion of a patient's arm (for example a stroke survivor) in a natural way, to accelerate recovery and improve the patient's quality of life

A Human Motor Control Framework based on Muscle Synergies

In spite of the complexities of the human musculoskeletal system, the central nervous system has the ability to orchestrate difficult motor tasks. Many researchers have tried to understand how the human nervous system works. Yet, our knowledge about the integration of sensory information and motor control is incomplete. This thesis presents a mathematical motor control framework that is developed to give the scientific community a biologically-plausible feedback controller for fast and efficient control of musculoskeletal systems. This motor control framework can be applied to musculoskeletal systems of various complexities, which makes it a viable tool for many predictive musculoskeletal simulations, assistive device design and control, and general motor control studies. The most important feature of this real-time motor control framework is its emphasis on the intended task. In this framework, a task is distinguished by the kinematic variables that need to be controlled. For example, in a reaching task, the task variables are the position of the hand (individual joint angles are irrelevant to the reaching task). Consequently, the task space is defined as the subspace that is formed by all the controlled variables. This motor control framework employs a hierarchical structure to speed up the calculations while maintaining high control efficiency. In this framework, there is a high-level controller, which deals with path planning and error compensation in the task space. The output of this task space controller is the acceleration vector in the task space, which needs to be fulfilled by muscle activities. The fast and efficient transformation of the task space accelerations to muscle activities in real-time is a main contribution of this research. Instead of using optimization to solve for the muscle activations (the usual practice in the past), this acceleration-to-activation (A2A) mapping uses muscle synergies to keep the computations simple enough to be real-time implementable. This A2A mapping takes advantage of the known effect of muscle synergies in the task space, thereby reducing the optimization problem to a vector decomposition problem. To make the result of the A2A mapping more efficient, the novel concept of posture-dependent synergies is introduced. The validity of the assumptions and the performance of the motor control framework are assessed using experimental trials. The experimental results show that the motor control framework can reconstruct the measured muscle activities only using the task-related kinematic/dynamic information. The application of the motor control framework to feedback motion control of musculoskeletal systems is also presented in this thesis. The framework is applied to musculoskeletal systems of various complexities (up to four-degree-of-freedom systems with 15 muscles) to show its effectiveness and generalizability to different dimensions. The control of functional electrical stimulation (FES) is another important application of my motor control framework. In FES, the muscles are activated by external electrical pulses to generate force, and consequently motion in paralysed limbs. There exists no feedback FES controller of upper extremity movements in the literature. The proposed motor control model is the first feedback FES controller that can be used for the control of reaching movements to arbitrary targets. Experimental results show that the motor control model is fast enough and accurate enough to be used as a practical motion controller for FES systems. Using such a biologically-plausible motor control model, it is possible to control the motion of a patient's arm (for example a stroke survivor) in a natural way, to accelerate recovery and improve the patient's quality of life

Development and Biomechanical Analysis toward a Mechanically Passive Wearable Shoulder Exoskeleton

Shoulder disability is a prevalent health issue associated with various orthopedic and neurological conditions, like rotator cuff tear and peripheral nerve injury. Many individuals with shoulder disability experience mild to moderate impairment and struggle with elevating the shoulder or holding the arm against gravity. To address this clinical need, I have focused my research on developing wearable passive exoskeletons that provide continuous at-home movement assistance. Through a combination of experiments and computational tools, I aim to optimize the design of these exoskeletons. In pursuit of this goal, I have designed, fabricated, and preliminarily evaluated a wearable, passive, cam-driven shoulder exoskeleton prototype. Notably, the exoskeleton features a modular spring-cam-wheel module, allowing customizable assistive force to compensate for different proportions of the shoulder elevation moment due to gravity. The results of my research demonstrated that this exoskeleton, providing modest one-fourth gravity moment compensation at the shoulder, can effectively reduce muscle activity, including deltoid and rotator cuff muscles. One crucial aspect of passive shoulder exoskeleton design is determining the optimal anti-gravity assistance level. I have addressed this challenge using computational tools and found that an assistance level within the range of 20-30% of the maximum gravity torque at the shoulder joint yields superior performance for specific shoulder functional tasks. When facing a new task dynamic, such as wearing a passive shoulder exoskeleton, the human neuro-musculoskeletal system adapts and modulates limb impedance at the end-limb (i.e., hand) to enhance task stability. I have presented development and validation of a realistic neuromusculoskeletal model of the upper limb that can predict stiffness modulation and motor adaptation in response to newly introduced environments and force fields. Future studies will explore the model\u27s applicability in predicting stiffness modulation for 3D movements in novel environments, such as passive assistive devices\u27 force fields

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

A computational neuromuscular model of the human upper airway with application to the study of obstructive sleep apnoea

Includes bibliographical references.Numerous challenges are faced in investigations aimed at developing a better understanding of the pathophysiology of obstructive sleep apnoea. The anatomy of the tongue and other upper airway tissues, and the ability to model their behaviour, is central to such investigations. In this thesis, details of the construction and development of a three-dimensional finite element model of soft tissues of the human upper airway, as well as a simplified fluid model of the airway, are provided. The anatomical data was obtained from the Visible Human Project, and its underlying micro-histological data describing tongue musculature were also extracted from the same source and incorporated into the model. An overview of the mathematical models used to describe tissue behaviour, both at a macro- and microscopic level, is given. Hyperelastic constitutive models were used to describe the material behaviour, and material incompressibility was accounted for. An active Hill three-element muscle model was used to represent the muscular tissue of the tongue. The neural stimulus for each muscle group to a priori unknown external forces was determined through the use of a genetic algorithm-based neural control model. The fundamental behaviour of the tongue under gravitational and breathing-induced loading is investigated. The response of the various muscles of the tongue to the complex loading developed during breathing is determined, with a particular focus being placed to that of the genioglossus. It is demonstrated that, when a time-dependent loading is applied to the tongue, the neural model is able to control the position of the tongue and produce a physiologically realistic response for the genioglossus. A comparison is then made to the response determined under quasi-static conditions using the pressure distribution extracted from computational fluid-dynamics results. An analytical model describing the time-dependent response of the components of the tongue musculature most active during oral breathing is developed and validated. It is then modified to simulate the activity of the tongue during sleep and under conditions relating to various possible neural and physiological pathologies. The retroglossal movement of the tongue resulting from the pathologies is quantified and their role in the potential to induce airway collapse is discussed