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

On the intrinsic control properties of muscle and relexes: exploring the interaction between neural and musculoskeletal dynamics in the framework of the equilbrium-point hypothesis

The aim of this thesis is to examine the relationship between the intrinsic dynamics of the body and its neural control. Specifically, it investigates the influence of musculoskeletal properties on the control signals needed for simple goal-directed movements in the framework of the equilibriumpoint (EP) hypothesis. To this end, muscle models of varying complexity are studied in isolation and when coupled to feedback laws derived from the EP hypothesis. It is demonstrated that the dynamical landscape formed by non-linear musculoskeletal models features a stable attractor in joint space whose properties, such as position, stiffness and viscosity, can be controlled through differential- and co-activation of antagonistic muscles. The emergence of this attractor creates a new level of control that reduces the system’s degrees of freedom and thus constitutes a low-level motor synergy. It is described how the properties of this stable equilibrium, as well as transient movement dynamics, depend on the various modelling assumptions underlying the muscle model. The EP hypothesis is then tested on a chosen musculoskeletal model by using an optimal feedback control approach: genetic algorithm optimisation is used to identify feedback gains that produce smooth single- and multijoint movements of varying amplitude and duration. The importance of different feedback components is studied for reproducing invariants observed in natural movement kinematics. The resulting controllers are demonstrated to cope with a plausible range of reflex delays, predict the use of velocity-error feedback for the fastest movements, and suggest that experimentally observed triphasic muscle bursts are an emergent feature rather than centrally planned. Also, control schemes which allow for simultaneous control of movement duration and distance are identified. Lastly, it is shown that the generic formulation of the EP hypothesis fails to account for the interaction torques arising in multijoint movements. Extensions are proposed which address this shortcoming while maintaining its two basic assumptions: control signals in positional rather than force-based frames of reference; and the primacy of control properties intrinsic to the body over internal models. It is concluded that the EP hypothesis cannot be rejected for single- or multijoint reaching movements based on claims that predicted movement kinematics are unrealistic

The Effects of Age-related Differences in State Estimation on Sensorimotor Control of the Arm in School-age Children

Previous research examining sensorimotor control of arm movements in school-age children has demonstrated age-related improvements in performance. A unifying, mechanistic explanation of these improvements is currently lacking. This dissertation systematically examined the processes involved in sensorimotor control of the arm to investigate the hypothesis that improvements in performance can be attributed, in part, to developmental changes in state estimation, defined as estimates computed by the central nervous system (CNS) that specify current and future hand positions and velocities (i.e., hand `state'). A series of behavioral experiments were employed in which 5- to 12-year-old children and adults executed goal-directed arm movements. Experiment 1 demonstrated that improvements in proprioceptive functioning resulted in an increased contribution of proprioception to the multisensory estimate of hand position, suggesting that the CNS of children flexibly integrates redundant sensorimotor feedback based on the accuracy of the individual inputs. Experiment 2 demonstrated that improvements in proprioceptive functioning for localizing initial hand position reduced the directional variability of goal-directed reaching, suggesting that improvements in static state estimation contribute to the age-related improvements in performance. Relying on sensory feedback to provide estimates of hand state during movement execution can result in erroneous movement trajectories due to delays in sensory processing. Research in adults has suggested that the CNS circumvents these delays by integrating sensory feedback with predictions of future hand states (i.e., dynamic state estimation), a finding that has not been investigated in children. Experiment 3 demonstrated that young children utilized delayed and unreliable state estimates to make on-line trajectory modifications, resulting in poor sensorimotor performance. Last, Experiment 4 hypothesized that if improvements in state estimation drive improvements in sensorimotor performance, then exposure to a perturbation that simulated the delayed and unreliable dynamic state estimation in young children would cause the adults to perform similarly to the young children (i.e., eliminating age-related improvements in performance). Results from this study were equivocal. Collectively, the results from these experiments: 1) characterized a developmental trajectory of state estimation across 5- to 12-year-old children; and, 2) demonstrated that the development of state estimation is one mechanism underlying the age-related improvements in sensorimotor performance

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

다중손가락 과제 수행 시 인간의 감각 및 인지 처리 과정의 정량화

학위논문 (박사) -- 서울대학교 대학원 : 사범대학 체육교육과, 2021. 2. 박재범.The continuously varied states of human body and surrounding environment require instantaneous motor adaptations and the understanding of motor goal to achieve desired actions. These sensory and cognitive processes have been investigated as elements in motor control during last five decades. Specially, the task dependency on sensory and cognitive processes suggest the effects of movement properties in terms of environment situation and motor goal. However, these effects were mostly empirically summarized with the measurements of either neural activity or simple motor accomplishment unilaterally. The current thesis addresses the quantification of sensory and cognitive processes based on simultaneous measurements of brain activity and synergic motor performance during multi-digit actions with different movement properties. Multi-digit action as a representation of synergic movements has developed into a widespread agency to quantify the efficacy of motor control, as the reason applied in this thesis. In this thesis, multi-digit rotation and pressing tasks were performed with different movement directions, frequencies, feedback modalities, or task complexities. (Chapter 3) The changes of movement direction induced a decrease in motor synergy but regardless of which direction. (Chapter 4 and 5) Increased frequency of rhythmic movement reduced synergic motor performance associate with decreased sensory process and less efficient cognitive process. (Chapter 6) More comprehensive feedback modality improved synergic performance with increased sensory process. (Chapter 7) Increased movement complexity had a consistent but stronger effect as increased frequency on synergic performance and efficiency of cognitive process. These observations suggest that several movement properties affect the contributions of sensory and cognitive processes to motor control which can be quantified through either neural activity or synergic motor performance. Accordingly, those movement properties may be applied in the rehabilitation of motor dysfunction by developing new training programs or assistant devices. Additionally, it may be possible to develop a simplified while efficient method to estimate the contribution of sensory or cognitive process to motor control.시시각각으로 변화하는 신체 상태와 주변 환경의 상호작용 속에서 알맞은 움직임을 수행하기 위해서는 그에 따른 즉각적인 운동 적응(motor adaptation) 과정와 과제 목표에 대한 이해가 필요하다. 이를 위해 인간의 감각 및 인지 처리과정은 운동 제어 분야의 중요한 요소로 여겨졌다. 선행연구에 따르면, 운동 과제에 따라 변화하는 감각 및 인지 처리과정은 주변 환경과 과제의 목표에 따라 움직임의 특성에 영향을 미친다고 보고되어왔다. 그러나 이러한 영향은 대부분 단순한 운동과제 수행 결과 또는 측정된 신경 활동에 의해 경험적으로 요약된 결과에 국한되어 있다. 따라서 본 논문은 다양한 움직임 특성을 가진 다중 손가락 과제 수행 시, 뇌 활동 (Brain activity)과 더불어 손가락들 간의 협응적인 움직임의 수행 결과를 동시 측정하여 과제의 특성에 따른 감각 및 인지 처리과정의 변화를 분석했다. 다중 손가락 과제는 운동 제어의 성능 효율성을 정량화하기 위해 사용되는 대표적인 과제다. 본 논문에서는 다양한 조건의 움직임 방향, 움직임의 주기빈도, 감각 피드백 양식 또는 과제 난이도에 따른 다중 손가락 회전 동작 및 힘 생성 과제를 사용했다. 연구 결과로는, (문단 3) 움직임 방향이 변화하기 전에 변화할 방향에 상관없이 협응적인 움직임이 악화되었다. (문단 4와 5) 움직임의 주기빈도가 증가할수록 협응적인 움직임이 악화됐으며, 이와 관련된 감각 및 인지 처리과정의 효율성도 감소되었다. (문단 6) 단일 감각 피드백 제공조건에 비해 종합적인 감각 피드백은 증가된 감각 처리과정과 함께 협응적인 움직임을 향상시켰다. (문단 7) 과제의 난이도가 증가할수록 협응적인 움직임과 인지 처리과정의 효율성은 감소되었으며, 움직임의 주기빈도 조건에 비해 과제의 난이도에 따라 협응적인 움직임과 인지 처리과정에 미치는 영향은 상대적으로 더 크게 나타났다. 이러한 결과는 움직임 특성에 따른 뇌 활동과 협응적인 과제 수해 결과를 통해 운동 제어 과정에서 감각 및 인지 처리과정의 기여정도를 정량화할 수 있다는 점을 시사한다. 따라서 움직임 특성에 따른 감각 및 인지 처리 과정의 기여정도의 변화는 운동 기능 장애를 가진 사람들의 새로운 재활 훈련 프로그램 및 움직임 보조 장치를 개발하기 위한 실험적인 근거로 적용될 수 있다. 또한 감각 또는 인지 과정이 운동 제어에 미치는 영향을 추정하기 위한 효율적인 방법을 개발하는데 도움이 될 것이다.Chapter 1. Introduction 1 1.1 Problem statement 1 1.2 Study objective 2 1.3 Organization of dissertation 3 Chapter 2. Background 6 2.1 Motor system 6 2.1.1 Ascending pathway 6 2.1.2 Descending pathway 8 2.1.3 Brain networks 9 2.2 Motor synergy 11 2.2.1 Synergy in performance 12 2.2.2 Synergy in muscles 13 2.2.3 Synergy in neurons 14 2.3 Motor control 15 2.1.1 Sensory process 16 2.1.2 Cognitive process 19 Chapter 3. Effect of movement direction: Multi-Finger Interaction and Synergies in Finger Flexion and Extension Force Production 23 3.1 Abstract 23 3.2 Introduction 24 3.3 Method 28 3.4 Results 35 3.4.1 Maximal voluntary contraction (MVC) force and finger independency 36 3.4.2 Timing indices 37 3.4.3 Multi-finger synergy indices in mode space 39 3.4.4 Multi-finger synergy indices in force space 43 3.5 Discussion 44 3.5.1 Finger independency during finger flexion and extension 44 3.5.2 Multi-finger synergies in force and mode spaces 46 3.5.3 Anticipatory synergy adjustment 48 Chapter 4. Effect of Frequency: Brain Oxygenation Magnitude and Mechanical Outcomes during Multi-Digit Rhythmic Rotation Task 51 4.1 Abstract 51 4.2 Introduction 51 4.3 Methods 55 4.4 Results 61 4.4.1 PET imaging 61 4.4.2 Finger forces 62 4.4.3 UCM analysis 64 4.4.4 Correlation between neural activation and mechanics 65 4.5 Discussion 66 4.5.1 Regions involved in feedback 67 4.5.2 Regions involved in feedforward 69 4.5.3 Corporation of feedforward and feedback 71 4.6 Conclusions 72 Chapter 5. Effect of frequency: Prefrontal Cortex Oxygenation during Multi-Digit Rhythmic Pressing Actions using fNIRS 74 5.1 Abstract 74 5.2 Introduction 74 5.3 Method 77 5.4 Results 84 5.4.1 Performance 84 5.4.2 Multi-digit coordination indices 84 5.4.3 Functional connectivity (FC) 87 5.5 Discussion 88 5.6 Conclusion 91 Chapter 6. Effect of Sensory Modality: Multi-Sensory Integration during Multi-Digit Rotation Task with Different Frequency 92 6.1 Abstract 92 6.2 Introduction 92 6.3 Method 94 6.4 Results 100 6.4.1 Performance 100 6.4.2 Multi-digit coordination indices 101 6.5 Discussion 101 6.6 Conclusion 103 Chapter 7. Effect of Task Complexity: Prefrontal Cortex Oxygenation during Multi-Digit Pressing Actions with Different Frequency Components 104 7.1 Abstract 104 7.2 Introduction 104 7.3 Method 106 7.4 Results 112 7.4.1 Performance 112 7.4.2 Multi-finger coordination indices 113 7.4.3 Functional connectivity (FC) 114 7.5 Discussion 115 7.5.1 Relation between Frequency and task complexity 115 7.5.2 Cognitive process in motor control 116 7.5.3 Relation between motor coordination and cognitive process 118 7.6 Conclusion 119 Chapter 8. Conclusions and Future Work 120 8.1 Summary of conclusions 120 8.2 Future work 121 Bibliography 122 Abstract in Korean 160Docto

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

Stochastic optimal control with learned dynamics models

The motor control of anthropomorphic robotic systems is a challenging computational task mainly because of the high levels of redundancies such systems exhibit. Optimality principles provide a general strategy to resolve such redundancies in a task driven fashion. In particular closed loop optimisation, i.e., optimal feedback control (OFC), has served as a successful motor control model as it unifies important concepts such as costs, noise, sensory feedback and internal models into a coherent mathematical framework. Realising OFC on realistic anthropomorphic systems however is non-trivial: Firstly, such systems have typically large dimensionality and nonlinear dynamics, in which case the optimisation problem becomes computationally intractable. Approximative methods, like the iterative linear quadratic gaussian (ILQG), have been proposed to avoid this, however the transfer of solutions from idealised simulations to real hardware systems has proved to be challenging. Secondly, OFC relies on an accurate description of the system dynamics, which for many realistic control systems may be unknown, difficult to estimate, or subject to frequent systematic changes. Thirdly, many (especially biologically inspired) systems suffer from significant state or control dependent sources of noise, which are difficult to model in a generally valid fashion. This thesis addresses these issues with the aim to realise efficient OFC for anthropomorphic manipulators. First we investigate the implementation of OFC laws on anthropomorphic hardware. Using ILQG we optimally control a high-dimensional anthropomorphic manipulator without having to specify an explicit inverse kinematics, inverse dynamics or feedback control law. We achieve this by introducing a novel cost function that accounts for the physical constraints of the robot and a dynamics formulation that resolves discontinuities in the dynamics. The experimental hardware results reveal the benefits of OFC over traditional (open loop) optimal controllers in terms of energy efficiency and compliance, properties that are crucial for the control of modern anthropomorphic manipulators. We then propose a new framework of OFC with learned dynamics (OFC-LD) that, unlike classic approaches, does not rely on analytic dynamics functions but rather updates the internal dynamics model continuously from sensorimotor plant feedback. We demonstrate how this approach can compensate for unknown dynamics and for complex dynamic perturbations in an online fashion. A specific advantage of a learned dynamics model is that it contains the stochastic information (i.e., noise) from the plant data, which corresponds to the uncertainty in the system. Consequently one can exploit this information within OFC-LD in order to produce control laws that minimise the uncertainty in the system. In the domain of antagonistically actuated systems this approach leads to improved motor performance, which is achieved by co-contracting antagonistic actuators in order to reduce the negative effects of the noise. Most importantly the shape and source of the noise is unknown a priory and is solely learned from plant data. The model is successfully tested on an antagonistic series elastic actuator (SEA) that we have built for this purpose. The proposed OFC-LD model is not only applicable to robotic systems but also proves to be very useful in the modelling of biological motor control phenomena and we show how our model can be used to predict a wide range of human impedance control patterns during both, stationary and adaptation tasks

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

A neural model of the motor control system

In this thesis I present the Recurrent Error-driven Adaptive Control Hierarchy (REACH); a large-scale spiking neuron model of the motor cortices and cerebellum of the motor control system. The REACH model consists of anatomically organized spiking neurons that control a nonlinear three-link arm to perform reaching and handwriting, while being able to adapt to unknown changes in arm dynamics and structure. I show that the REACH model accounts for data across 19 clinical and experimental studies of the motor control system. These data includes a mix of behavioural and neural spiking activity, across normal and damaged subjects performing adaptive and static tasks. The REACH model is a dynamical control system based on modern control theoretic methods, specifically operational space control, dynamic movement primitives, and nonlinear adaptive control. The model is implemented in spiking neurons using the Neural Engineering Framework (NEF). The model plans trajectories in end-effector space, and transforms these commands into joint torques that can be sent to the arm simulation. Adaptive components of the model are able to compensate for unknown kinematic or dynamic system parameters, such as arm segment length or mass. Using the NEF the adaptive components of the system can be seeded with approximations of the system kinematics and dynamics, allowing faster convergence to stability. Stability proofs for nonlinear adaptation methods implemented in distributed systems with scalar output are presented. By implementing the motor control model in spiking neurons, biological constraints such as neurotransmitter time-constants and anatomical connectivity can be imposed, allowing further comparison to experimental data for model validation. The REACH model is compared to clinical data from human patients as well as neural recording from monkeys performing reaching experiments. The REACH model represents a novel integration of control theoretic methods and neuroscientific constraints to specify a general, adaptive, biologically plausible motor control algorithm.4 month