Neuromechanical Tuning for Arm Motor Control

Movement is a fundamental behavior that allows us to interact with the external world. Its importance to human health is most evident when it becomes impaired due to disease or injury. Physical and occupational rehabilitation remains the most common treatment for these types of disorders. Although therapeutic interventions may improve motor function, residual deficits are common for many pathologies, such as stroke. The development of novel therapeutics is dependent upon a better understanding of the underlying mechanisms that govern movement. Movement of the human body adheres to the principles of classic Newtonian mechanics. However, due to the inherent complexity of the body and the highly variable repertoire of environmental contexts in which it operates, the musculoskeletal system presents a challenging control problem and the onus is on the central nervous system to reliably solve this problem. The neural motor system is comprised of numerous efferent and afferent pathways with a hierarchical organization which create a complex arrangement of feedforward and feedback circuits. However, the strategy that the neural motor system employs to reliably control these complex mechanics is still unknown. This dissertation will investigate the neural control of mechanics employing a “bottom-up” approach. It is organized into three research chapters with an additional introductory chapter and a chapter addressing final conclusions. Chapter 1 provides a brief description of the anatomical and physiological principles of the human motor system and the challenges and strategies that may be employed to control it. Chapter 2 describes a computational study where we developed a musculoskeletal model of the upper limb to investigate the complex mechanical interactions due to muscle geometry. Muscle lengths and moment arms contribute to force and torque generation, but the inherent redundancy of these actuators create a high-dimensional control problem. By characterizing these relationships, we found mechanical coupling of muscle lengths which the nervous system could exploit. Chapter 3 describes a study of muscle spindle contribution to muscle coactivation using a computational model of primary afferent activity. We investigated whether these afferents could contribute to motoneuron recruitment during voluntary reaching tasks in humans and found that afferent activity was orthogonal to that of muscle activity. Chapter 4 describes a study of the role of the descending corticospinal tract in the compensation of limb dynamics during arm reaching movements. We found evidence that corticospinal excitability is modulated in proportion to muscle activity and that the coefficients of proportionality vary in the course of these movements. Finally, further questions and future directions for this work are discussed in the Chapter 5

Limits to temporal synchronization in fundamental hand and finger actions

Coordinated movement is critical not only to sports technique and performance but to daily living and as such represents a fundamental area of research. Coordination requires being able to produce the right actions at the right time and has to incorporate perception, cognition, and forceful neuro-muscular interaction with the environment. Coordinated movements of the hands and fingers are some of the most complex activities undertaken where continuous learning and adaptation take place, but the temporal variability of the most basic movement components is still unknown. This thesis investigates the extent of temporal variability in the execution of four different simple hand and finger coordination tasks, with the purpose to find the various intrinsic temporal variability which limit the ability to coordinate the hands in space and time. Study one showed that in a synchronized bi-lateral two finger tapping test (<<1 cm movement to target) the best participant had a temporaltiming variability of 4.8 ms whereas the largest time variability could be as high as 24.8 ms. No obvious improvement was found after transfer practice, whereas the average time variability for asynchronized tapping decreased from 62.1 ms to 30.3 ms after instructed practice indicating a likely change in task grouping. Study two showed that in a unilateral thumb-index finger pinch and release test, the largest mean timing variability was 12 ms for pinching irrespective of performing the task in a slow alert manner or at a faster speed. However, the mean temporal variability for release was only 6.3 ms when the task was performed in a more alert manner and indicates that release is more accurately controlled temporally than grip. Study three suggested that in a unilateral sagittal plane throwing action of the lower arm and hand, that elbow and wrist coordination for dynamic index finger tip location was better with a radial-ulnar deviation, darts-type, throwing action than a wrist flexor-extensor type action, basketball free throw type action (the mean variability was 37.5 ms and 27.2 ms, respectively). Study four compared the variability in bi-lateral finger tapping between voluntary tapping and involuntary finger contraction tapping. Electrically stimulated neural contractions had significantly lower force onset variability than voluntary or direct magnetic stimulation of muscles (6 ms, 9.5 ms, and 10.3 ms for electrically stimulated, voluntary and Transcranial Magnetic Stimulation stimulated contraction). This work provides a comprehensive analysis of the temporal variability in various fundamental digital movement tasks that can aid with the understanding of basic human coordination in sporting, daily living and clinical areas

Experimental muscle pain increases normalized variability of multidirectional forces during isometric contractions

Pain elicits complex adaptations of motor strategy, leading to impairments in the generation and control of steady forces, which depend on muscle architecture. The present study used a cross-over design to assess the effects of muscle pain on the stability of multidirectional (taskrelated and tangential) forces during sustained dorsiflexions, elbow flexions, knee extensions, and plantarflexions. Fifteen healthy subjects performed series of isometric contractions (13-s duration, 2.5, 20, 50, 70% of maximal voluntary force) before, during, and after experimental muscle pain. Three-dimensional force magnitude, angle and variability were measured while the task-related force was provided as feedback to the subjects. Surface electromyography was recorded from agonist and antagonist muscles. Pain was induced in agonist muscles by intramuscular injections of hypertonic (6%) saline with isotonic (0.9%) saline injections as control. The pain intensity was assessed on an electronic visual analogue scale. Experimental muscle pain elicited larger ranges of force angle during knee extensions and plantarflexions (P < 0.03) and higher normalized fluctuations of task-related (P < 0.02) and tangential forces (P < 0.03) compared with control assessments across force levels, while the mean force magnitudes, mean force angle and the level of muscle activity were non-significantly affected by pain. Increased multidirectional force fluctuations probably resulted from multiple mechanisms that, acting together, balanced the mean surface electromyography. Although pain adaptations are believed to aim at the protection of the painful site, the current results show that they result in impairments in steadiness of force

THE EFFECTS OF AGING ON MULTIPLE POSTURAL MUSCLE CONTROL AND POSTURAL SWAY BEHAVIOR

Episodes of instability and falls in the elderly represent a major public health concern. The lack of scientific information about the effects of age-related changes on neurophysiological mechanisms of postural control has limited the advance in the field of fall prevention and rehabilitation of balance disorders. The overall goal of this dissertation was to investigate the effects of aging on postural control. Considering the progressive non-homogeneous deterioration of aging physiological systems, a series of five experimental studies, with healthy young and healthy nonfaller older adults performing upright stance tasks, explored three main hypotheses: (1) intermuscular coherence analysis is able to detect signs of intermuscular synchronization at lower frequency bands as one of the strategies used by the Central Nervous System to control upright stance; (2) aging is associated with a reorganization of correlated neural inputs controlling postural muscles; and (3) aging is associated with changes in body sway behavior. The first three studies corroborated the use of intermuscular coherence analysis to investigate the formation of correlated neural inputs forming postural muscle synergies during upright stance. The fourth study revealed an age-related reorganization of the distribution and strength of correlated neural inputs to multiple postural muscles. Healthy nonfaller older adults presented stronger levels of synchronization, within 0–10 Hz, for three distinct muscle groups: anterior, posterior, and antagonist muscle groups. The fifth study investigated age-related changes on postural sway using traditional and novel postural indices extracted from the center of pressure coordinates. Although the functional base support is preserved in healthy nonfaller older adults, these seniors revealed a larger, faster, shakier, and more irregular pattern of body sway compared to healthy young adults. In addition, age-related changes on supraspinal mechanisms, spinal reflexes, and intrinsic mechanical properties of muscles and joints involved in postural control were observed by changes in both rambling and trembling components of the postural sway. Findings reported here provide valuable information regarding compensatory mechanisms adopted by healthy nonfaller older adults to control upright stance. Together, these findings suggest an age-related reorganization of correlated neural inputs controlling multiple postural muscles, accompanied by changes in body sway behavior

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

A Biomimetic Approach to Controlling Restorative Robotics

Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control. Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands. Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques. Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury. Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control.Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands.Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques.Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury

Effects of Movement Context on Reach-Grasp-Lift Motion and Grip Force after Stroke

Loss of upper extremity function after stroke is a significant problem resulting in enormous personal, societal, and economic costs. Neurophysiological discoveries over several decades have revealed great potential for use-dependent neural adaptation, and have revitalized the search for training strategies that optimize recovery. Although task-specific repetitive practice is recognized as a key stimulus to promote upper extremity function after stroke, choices of what to practice and how to practice remain challenging and poorly guided by evidence. This research was inspired by evidence in healthy individuals, that movement can be altered by characteristics of the task and the environment, together referred to as the movement context. The purpose of this research was to determine whether motor performance of the paretic upper extremity is affected by two specific movement context variations: 1) preferred speed versus fast, and 2) unilateral versus bilateral. Using electromagnetic motion tracking and pressure sensor quantification of grip force, we assessed upper extremity task performance in people with post-stroke hemiparesis. To evaluate effects of movement speed, we compared paretic-limb performance of a reach-grasp-lift task at a self-selected preferred speed to the same task performed as fast as possible. People with hemiparesis were able to move faster than their preferred speed, and when they did, movement quality was better. Reach paths were straighter, finger movements were more efficient, and the fingers opened wider. To evaluate effects of the bilateral movement context, we compared paretic-limb performance of a reach-grasp-lift-release task unilaterally versus bilaterally. We found no immediate improvement in the bilateral context. We further explored effects of the bilateral movement context by measuring maximal and submaximal grip force capacity using grip dynamometers. Unlike healthy controls and unlike the non-paretic side, the paretic side of people with hemiparesis produced more maximal force in the bilateral condition. In a submaximal task, however, the bilateral condition did not enhance the paretic side\u27s contribution. These results suggest that emphasizing speed during post-stroke rehabilitation may be worthwhile, that the bilateral movement context has little immediate impact on task performance, and that the paretic limb may benefit from the bilateral condition only at high force levels

Muscle activation mapping of skeletal hand motion: an evolutionary approach.

Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion