Understanding motor control in humans to improve rehabilitation robots

Recent reviews highlighted the limited results of robotic rehabilitation and the low quality of evidences in this field. Despite the worldwide presence of several robotic infrastructures, there is still a lack of knowledge about the capabilities of robotic training effect on the neural control of movement. To fill this gap, a step back to motor neuroscience is needed: the understanding how the brain works in the generation of movements, how it adapts to changes and how it acquires new motor skills is fundamental. This is the rationale behind my PhD project and the contents of this thesis: all the studies included in fact examined changes in motor control due to different destabilizing conditions, ranging from external perturbations, to self-generated disturbances, to pathological conditions. Data on healthy and impaired adults have been collected and quantitative and objective information about kinematics, dynamics, performance and learning were obtained for the investigation of motor control and skill learning. Results on subjects with cervical dystonia show how important assessment is: possibly adequate treatments are missing because the physiological and pathological mechanisms underlying sensorimotor control are not routinely addressed in clinical practice. These results showed how sensory function is crucial for motor control. The relevance of proprioception in motor control and learning is evident also in a second study. This study, performed on healthy subjects, showed that stiffness control is associated with worse robustness to external perturbations and worse learning, which can be attributed to the lower sensitiveness while moving or co-activating. On the other hand, we found that the combination of higher reliance on proprioception with \u201cdisturbance training\u201d is able to lead to a better learning and better robustness. This is in line with recent findings showing that variability may facilitate learning and thus can be exploited for sensorimotor recovery. Based on these results, in a third study, we asked participants to use the more robust and efficient strategy in order to investigate the control policies used to reject disturbances. We found that control is non-linear and we associated this non-linearity with intermittent control. As the name says, intermittent control is characterized by open loop intervals, in which movements are not actively controlled. We exploited the intermittent control paradigm for other two modeling studies. In these studies we have shown how robust is this model, evaluating it in two complex situations, the coordination of two joints for postural balance and the coordination of two different balancing tasks. It is an intriguing issue, to be addressed in future studies, to consider how learning affects intermittency and how this can be exploited to enhance learning or recovery. The approach, that can exploit the results of this thesis, is the computational neurorehabilitation, which mathematically models the mechanisms underlying the rehabilitation process, with the aim of optimizing the individual treatment of patients. Integrating models of sensorimotor control during robotic neurorehabilitation, might lead to robots that are fully adaptable to the level of impairment of the patient and able to change their behavior accordingly to the patient\u2019s intention. This is one of the goals for the development of rehabilitation robotics and in particular of Wristbot, our robot for wrist rehabilitation: combining proper assessment and training protocols, based on motor control paradigms, will maximize robotic rehabilitation effects

An Optimal Control Model for Human Postural Regulation

Human upright stance is inherently unstable without a balance control scheme. Many biological behaviors are likely to be optimal with respect to some performance measure that involves energy. It is reasonable to believe that the human is (unconsciously) optimizing some performance measure as he regulates his balance posture. In experimental studies, a notable feature of postural control is a small constant sway. Specifically, there is greater sway than would occur with a linear feedback control without delay. A second notable feature of the human postural control is that the response to perturbations varies with their amplitude. Small disturbances produce motion only at the ankles with the hip and knee angles unchanging. Large perturbation evoke ankle and hip angular movement only. Still larger perturbation result in movement of all three joint angles. Inspired by these features, a biomechanical model resembling human balance control is proposed. The proposed model consists of three main components which are the body dynamics, a sensory estimator for delay and disturbance, and an optimal nonlinear control scheme providing minimum required corrective response. The human body is modeled as a multiple segment inverted pendulum in the sagittal plane and controlled by ankle and hip joint torques. A series of nonlinear optimal control problems are devised as mathematical models of human postural control during quiet standing. Several performance criteria that are high even orders in the body state or functions of these states (such as joint angle, Center of Pressure COP or Center of Mass COM) and quadratic in the joint control are utilized. This objective function provides a trade-off between the allowed deviations of the position from its nominal value and the neuromuscular energy required to correct for these deviations. Note that this performance measure reduces the actuator energy used by penalizing small postural errors very lightly. By using the Model Predictive Control (MPC) technique, the discrete-time approximation to each of these problems can be converted into a nonlinear programming problem and then solved by optimization methods. The solution gives a control scheme that agrees with the main features of the joint kinematics and its coordination process. The derived model is simulated for different scenarios to validate and test the performance of the proposed postural control architecture

Virtual reality obstacle crossing: adaptation, retention and transfer to the physical world

Virtual reality (VR) paradigms are increasingly being used in movement and exercise sciences with the aim to enhance motor function and stimulate motor adaptation in healthy and pathological conditions. Locomotor training based in VR may be promising for motor skill learning, with transfer of VR skills to the physical world in turn required to benefit functional activities of daily life. This PhD project aims to examine locomotor adaptations to repeated VR obstacle crossing in healthy young adults as well as transfers to the untrained limb and the physical world, and retention potential of the learned skills. For these reasons, the current thesis comprises three studies using controlled VR obstacle crossing interventions during treadmill walking. In the first and second studies we investigated adaptation to crossing unexpectedly appearing virtual obstacles, with and without feedback about crossing performance, and its transfer to the untrained leg. In the third study we investigated transfer of virtual obstacle crossing to physical obstacles of similar size to the virtual ones, that appeared at the same time point within the gait cycle. We also investigated whether the learned skills can be retained in each of the environments over one week. In all studies participants were asked to walk on a treadmill while wearing a VR headset that represented their body as an avatar via real-time synchronised optical motion capture. Participants had to cross virtual and/or physical obstacles with and without feedback about their crossing performance. If applicable, feedback was provided based on motion capture immediately after virtual obstacle crossing. Toe clearance, margin of stability, and lower extremity joint angles in the sagittal plane were calculated for the crossing legs to analyse adaptation, transfer, and retention of obstacle crossing performance. The main outcomes of the first and second studies were that crossing multiple virtual obstacles increased participants’ dynamic stability and led to a nonlinear adaptation of toe clearance that was enhanced by visual feedback about crossing performance. However, independent of the use of feedback, no transfer to the untrained leg was detected. Moreover, despite significant and rapid adaptive changes in locomotor kinematics with repeated VR obstacle crossing, results of the third study revealed limited transfer of learned skills from virtual to physical obstacles. Lastly, despite full retention over one week in the virtual environment we found only partial retention when crossing a physical obstacle while walking on the treadmill. In summary, the findings of this PhD project confirmed that repeated VR obstacle perturbations can effectively stimulate locomotor skill adaptations. However, these are not transferable to the untrained limb irrespective of enhanced awareness and feedback. Moreover, the current data provide evidence that, despite significant adaptive changes in locomotion kinematics with repeated practice of obstacle crossing under VR conditions, transfer to and retention in the physical environment is limited. It may be that perception-action coupling in the virtual environment, and thus sensorimotor coordination, differs from the physical world, potentially inhibiting retained transfer between those two conditions. Accordingly, VR-based locomotor skill training paradigms need to be considered carefully if they are to replace training in the physical world

Prescription of Ankle-Foot Orthoses for Children with Cerebral Palsy

Purpose: Ankle foot orthoses (AFOs) are frequently prescribed to address gait impairments for children with cerebral palsy (CP). Successful treatment with AFOs depends on optimal prescription, matching the design of the brace to the individual child’s physical impairments; however, research evidence does not exist to help health care professionals decide on the best AFO design to meet each child’s needs. Therefore, this thesis explored current AFO prescription practices, and aimed to improve evidence to assist clinicians in making prescription decisions for children with CP. Methods and Results: To examine the experiences and perspectives of clinicians on AFO prescription for children with CP, we conducted focus groups and semi-structured interviews with 32 clinicians who were involved with AFO prescription for children with CP in five Canadian rehabilitation facilities. Using Interpretive Description as a framework for analysis, we identified three categories from the data: 1) What is made, 2) How it is used, and 3) Factors that support or challenge outcomes. Throughout the interviews, the theme of prescription as a collaborative, iterative, and individualized process emerged. To explore evaluation and clinical decision-making practices of physical therapists for AFO prescription and follow-up, we invited Canadian physical therapists (PTs) working with children who have CP to complete an online survey. Sixty completed responses were received. Three researchers conducted a conventional content analysis to examine the open-ended responses, and descriptive statistics were used to summarize the closed-ended responses. Three themes were identified: 1) Focus on impairment level measures, 2) Inconsistent practices between PTs, and 3) Lack of confidence/knowledge about casting positions and AFO types. To investigate the effects of individualizing the angle of the ankle in the AFO on walking mechanics and function, gait biomechanics were studied in ten children with CP. Fifteen typically-developing children provided normative data. Using three-dimensional gait analysis, kinematics and kinetics were compared between the child’s usual AFO(s) and AFOs that were fabricated with an ankle angle that was individualized for each child. Net responses to the individualized ankle angle were positive for 60% of limbs, negative for 40%. The greatest benefits were observed at the knee, suggesting that this may be a beneficial approach to orthotic intervention for some children with CP. Conclusions: There is limited understanding of how AFOs are prescribed for children with CP in Canada. This thesis highlights the importance of multidisciplinary collaboration, objective evaluation, and individualized clinical problem-solving to facilitate the evolution of the AFO prescription from a medical directive to an orthotic device that optimally benefits the child. This is the first step toward the development of guidelines to help clinicians improve AFO prescription for children with CP

A Lower Limb Prosthesis with Active Alignment for Reduced Limb Loading

Over the past decade, the growing field of robotics has created new possibilities in lower limb prostheses. The focus of these new prostheses has been replicating the dynamics of the lost limb in order to restore gait of individuals with lower limb amputations to healthy norms. This places demanding loads on the residual limb. Compensation by the rest of body is high, causes overloading of intact joints and can lead to deterioration of mobility and overall health. Abnormalities remain present in the person’s gait, stemming from the loading of soft tissue and the altered anatomy of the affected limb. In this dissertation, an experimental prosthesis is developed with systematic, simulation based techniques. Kinematics and kinetics of the prosthesis design are altered in order to actively realign the limb in relation to the center of pressure during stance, allowing positive power to be generated by the prosthesis while actively reducing the magnitude of the sagittal moment transferred to the residual limb. Initial findings show that during walking with the experimental device compared to a daily use prosthesis, peak pressures on the residual limb are lowered by over 10% while maintaining walking speed

Dynamic Balance and Gait Metrics for Robotic Bipeds

For legged robots to be useful in the real world, they must be able to balance and walk reliably. Both of these abilities improve when a system is more effective at moving itself around relative to its contacts (i.e., its feet). Achieving this type of movement depends both on the controller used to perform the motion and the physical properties of the system. Although much work has been done on the development of dynamic controllers for balance and gait, only limited research exists on how to quantify a system’s physical balance capabilities or how to modify the system to improve those capabilities. From the control perspective, there are three strategies for maintaining balance in bipeds: flexing, leaning, and stepping. Both stepping and leaning strategies typically depend on balance points (critical points used for maintaining or regaining balance) to determine whether or not a step is needed, and if so, where to step. Although several balance point estimators exist, the majority of these methods make undesirable assumptions (e.g., ignoring the impact dynamics, assuming massless legs, planar motion, etc.). From the physical design perspective, one promising approach for analyzing system performance is a set of dynamic ratios called velocity and momentum gains, which are dependent only on the (scale-invariant) dynamic parameters and instantaneous configuration of a system, enabling entire classes of mechanisms to be analyzed at the same time. This thesis makes four key contributions towards improving biped balancing capabilities. First, a dynamic bipedal controller is proposed which uses a 3D balance point estimator both to respond to disturbances and produce reliable stepping. Second, a novel balance point estimator is proposed that facilitates stepping while combining and expanding the features of existing 2D and 3D estimators to produce a generalized 3D formulation. Third, the momentum gain formulation is extended to general 2D and 3D systems, then both gains are compared to centroidal momentum via a spatial formulation and incorporated into a generalized gain definition. Finally, the gains are used as a metric in an optimization framework to design parameterized balancing mechanisms within a given configuration space. Effectively, this enables an optimization of how well a system could balance without the need to pre-specify or co-generate controllers and/or trajectories. To validate the control contributions, simulated bipeds are subjected to external disturbances while standing still and walking. For the gain contributions, the framework is used to compare gain-optimized mechanisms to those based on the cost of transport metric. Through the combination of gain-based physical design optimization and the use of predictive, real-time balance point estimators within dynamic controllers, bipeds and other legged systems will soon be able to achieve reliable balance and gait in the real world

Bioinspired robotic rehabilitation tool for lower limb motor learning after stroke

Mención Internacional en el título de doctorEsta tesis doctoral presenta, tras repasar la marcha humana, las principales patologíıas y condiciones que la afectan, y los distintos enfoques de rehabilitación con la correspondiente implicación neurofisiológica, el camino de investigación que desemboca en la herramienta robótica de rehabilitación y las terapias que se han desarrollado en el marco de los proyectos europeos BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills y HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients, y probado bajo el paraguas del proyecto europeo ASTONISH: Advancing Smart Optical Imaging and Sensing for Health y el proyecto nacional ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This doctoral thesis presents, after reviewing human gait, the main pathologies and conditions that affect it, and the different rehabilitation approaches with the corresponding neurophysiological implications, the research journey that leads to the development of the rehabilitation robotic tool, and the therapies that have been designed, within the framework of the European projects BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills and HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients and tested under the umbrella of the European project ASTONISH: Advancing Smart Optical Imaging and Sensing for Health and the national project ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This work has been carried out at the Neural Rehabilitation Group (NRG), Cajal Institute, Spanish National Research Council (CSIC). The research presented in this thesis has been funded by the Commission of the European Union under the BioMot project - Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT - 611695); under HANK Project - European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients (Grant Agreements number H2020-EU.2. - PRIORITY ’Industrial leadership’ and H2020-EU.3. - PRIORITY ’Societal challenges’ - 699796); also under the ASTONISH Project - Advancing Smart Optical Imaging and Sensing for Health (Grant Agreement number H2020-EU.2.1.1.7. - ECSEL - 692470); with financial support of Spanish Ministry of Economy and Competitiveness (MINECO) under the ASSOCIATE project - A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury (Grant Agreement number 799158449-58449-45-514); and with grant RYC-2014-16613, also by Spanish Ministry of Economy and Competitiveness.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fernando Javier Brunetti Fernández.- Secretario: Dorin Sabin Copaci.- Vocal: Antonio Olivier

Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+

Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios. Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations. In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped. Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs. Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia. The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred

Locomoção de humanoides robusta e versátil baseada em controlo analítico e física residual

Humanoid robots are made to resemble humans but their locomotion abilities are far from ours in terms of agility and versatility. When humans walk on complex terrains or face external disturbances, they combine a set of strategies, unconsciously and efficiently, to regain stability. This thesis tackles the problem of developing a robust omnidirectional walking framework, which is able to generate versatile and agile locomotion on complex terrains. We designed and developed model-based and model-free walk engines and formulated the controllers using different approaches including classical and optimal control schemes and validated their performance through simulations and experiments. These frameworks have hierarchical structures that are composed of several layers. These layers are composed of several modules that are connected together to fade the complexity and increase the flexibility of the proposed frameworks. Additionally, they can be easily and quickly deployed on different platforms. Besides, we believe that using machine learning on top of analytical approaches is a key to open doors for humanoid robots to step out of laboratories. We proposed a tight coupling between analytical control and deep reinforcement learning. We augmented our analytical controller with reinforcement learning modules to learn how to regulate the walk engine parameters (planners and controllers) adaptively and generate residuals to adjust the robot’s target joint positions (residual physics). The effectiveness of the proposed frameworks was demonstrated and evaluated across a set of challenging simulation scenarios. The robot was able to generalize what it learned in one scenario, by displaying human-like locomotion skills in unforeseen circumstances, even in the presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos, mas suas habilidades de locomoção estão longe das nossas em termos de agilidade e versatilidade. Quando os humanos caminham em terrenos complexos ou enfrentam distúrbios externos combinam diferentes estratégias, de forma inconsciente e eficiente, para recuperar a estabilidade. Esta tese aborda o problema de desenvolver um sistema robusto para andar de forma omnidirecional, capaz de gerar uma locomoção para robôs humanoides versátil e ágil em terrenos complexos. Projetámos e desenvolvemos motores de locomoção sem modelos e baseados em modelos. Formulámos os controladores usando diferentes abordagens, incluindo esquemas de controlo clássicos e ideais, e validámos o seu desempenho por meio de simulações e experiências reais. Estes frameworks têm estruturas hierárquicas compostas por várias camadas. Essas camadas são compostas por vários módulos que são conectados entre si para diminuir a complexidade e aumentar a flexibilidade dos frameworks propostos. Adicionalmente, o sistema pode ser implementado em diferentes plataformas de forma fácil. Acreditamos que o uso de aprendizagem automática sobre abordagens analíticas é a chave para abrir as portas para robôs humanoides saírem dos laboratórios. Propusemos um forte acoplamento entre controlo analítico e aprendizagem profunda por reforço. Expandimos o nosso controlador analítico com módulos de aprendizagem por reforço para aprender como regular os parâmetros do motor de caminhada (planeadores e controladores) de forma adaptativa e gerar resíduos para ajustar as posições das juntas alvo do robô (física residual). A eficácia das estruturas propostas foi demonstrada e avaliada em um conjunto de cenários de simulação desafiadores. O robô foi capaz de generalizar o que aprendeu em um cenário, exibindo habilidades de locomoção humanas em circunstâncias imprevistas, mesmo na presença de ruído e impulsos externos.Programa Doutoral em Informátic