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

Implementation of a Human Feedback-based Locomotion and its Control by means of a Feedforward Component inspired by Central Pattern Generators

Walking gaits of various animals have been modeled using this framework of differential equations, and more specifically, using network of coupled oscillators or CPG (Central Pattern Generators). In these models, oscillators are coupled among themselves and thus influence each other. Nerve signals generating swimming in the Lampreys have been modeled using such a system. These oscillatory networks were successfully used to model several swimming animals. However, results obtained with walking animals have been rather disappointing. This is not surprising, since CPGs does not take into account interaction with the environment for shaping the movement, which seems to be more important for walking animals. In this project we study the interaction between feedback and CPGs in humans using a bio-inspired musculoskeletal model of human walking. We start with a pure feedback based model of human walking and extend it by introducing a feedforward component inspired by CPGs. We then test the properties of such a hybrid feedback and feedforward system. We show that, not only those new models are stable with characteristics close to the original model, but with online control they showed a clear increase of the robustness compared to pure feedback model. Moreover, modifications of some general parameters of the feedforward component allow easy changes in gait characteristics, such as gait speed

From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

The Runbot: engineering control applied to rehabilitation in spinal cord injury patients

Human walking is a complicated interaction among the musculoskeletal system, nervous system and the environment. An injury affecting the neurological system, such as a spinal cord injury (SCI) can cause sensor and motor deficits, and can result in a partial or complete loss of their ambulatory functions. Functional electrical stimulation (FES), a technique to generate artificial muscle contractions with the application of electrical current, has been shown to improve the ambulatory ability of patients with an SCI. FES walking systems have been used as a neural prosthesis to assist patients walking, but further work is needed to establish a system with reduced engineering complexity which more closely resembles the pattern of natural walking. The aim of this thesis was to develop a new FES gait assistance system with a simple and efficient FES control based on insights from robotic walking models, which can be used in patients with neuromuscular dysfunction, for example in SCI. The understanding of human walking is fundamental to develop suitable control strategies. Limit cycle walkers are capable of walking with reduced mechanical complexity and simple control. Walking robots based on this principle allow bio-inspired mechanisms to be analysed and validated in a real environment. The Runbot is a bipedal walker which has been developed based on models of reflexes in the human central nervous system, without the need for a precise trajectory algorithm. Instead, the timing of the control pattern is based on ground contact information. Taking the inspiration of bio-inspired robotic control, two primary objectives were addressed. Firstly, the development of a new reflexive controller with the addition of ankle control. Secondly, the development of a new FES walking system with an FES control model derived from the principles of the robotic control system. The control model of the original Runbot utilized a model of neuronal firing processes based on the complexity of the central neural system. As a causal relationship between foot contact information and muscle activity during human walking has been established, the control model was simplified using filter functions that transfer the sensory inputs into motor outputs, based on experimental observations in humans. The transfer functions were applied to the RunBot II to generate a stable walking pattern. A control system for walking was created, based on linear transfer functions and ground reaction information. The new control system also includes ankle control, which has not been considered before. The controller was validated in experiments with the new RunBot III. The successful generation of stable walking with the implementation of the novel reflexive robotic controller indicates that the control system has the potential to be used in controlling the strategies in neural prosthesis for the retraining of an efficient and effective gait. To aid of the development of the FES walking system, a reliable and practical gait phase detection system was firstly developed to provide correct ground contact information and trigger timing for the control. The reliability of the system was investigated in experiments with ten able-bodied subjects. Secondly, an automatic FES walking system was implemented, which can apply stimulation to eight muscles (four in each leg) in synchrony with the user’s walking activity. The feasibility and effectiveness of this system for gait assistance was demonstrated with an experiment in seven able-bodied participants. This thesis addresses the feasibility and effectiveness of applying biomimetic robotic control principles to FES control. The interaction among robotic control, biology and FES control in assistive neural prosthesis provides a novel framework to developing an efficient and effective control system that can be applied in various control applications

Neuromuscular Reflex Control for Prostheses and Exoskeletons

Recent powered lower-limb prosthetic and orthotic (P/O) devices aim to restore legged mobility for persons with an amputation or spinal cord injury. Though various control strategies have been proposed for these devices, specifically finite-state impedance controllers, natural gait mechanics are not usually achieved. The goal of this project was to invent a biologically-inspired controller for powered P/O devices. We hypothesize that a more muscle-like actuation system, including spinal reflexes and vestibular feedback, can achieve able-bodied walking and also respond to outside perturbations. The outputs of the Virtual Muscle Reflex (VMR) controller are joint torque commands, sent to the electric motors of a P/O device. We identified the controller parameters through optimizations using human experimental data of perturbed walking, in which we minimized the error between the torque produced by our controller and the standard torque trajectories observed in the able-bodied experiments. In simulations, we then compare the VMR controller to a four-phase impedance controller. For both controllers the coefficient of determination R^2 and root-mean-square (RMS) error were calculated as a function of the gait cycle. When simulating the hip, knee, and ankle joints, the RMS error and R^2 across all joints and all trials is 15.65 Nm and 0.28 for the impedance controller, respectively, and for the VMR controller, these values are 15.15 Nm and 0.29, respectively. With similar performance, it was concluded that the VMR controller can reproduce characteristics of human walking in response to perturbations as effectively as an impedance controller. We then implemented the VMR controller on the Parker Hannifin powered exoskeleton and performed standard isokinetic and isometric knee rehabilitation exercises to observe the behavior of the virtual muscle model. In the isometric results, RMS error between the measured and commanded extension and flexion torques are 3.28 Nm and 1.25 Nm, respectively. In the isokinetic trials, we receive RMS error between the measured and commanded extension and flexion torques of 0.73 Nm and 0.24 Nm. Since the onboard virtual muscles demonstrate similar muscle force-length and force-velocity relationships observed in humans, we conclude the model is capable of the same stabilizing capabilities as observed in an impedance controller

Biped locomotion control through a biologically-inspired closed-loop controller

Dissertação de mestrado integrado em Engenharia BiomédicaCurrently motor disability in industrialized countries due to neural and physical impairments is an increasingly worrying phenomenon and the percentage of patients is expected to be increasing continuously over the coming decades due to a process of ageing the world is undergoing. Additionally, rising retirement ages, higher demand of elderly people for an independent, dignified life and mobility, huge cost in the provision of health care are some other determinants that motivate the restoration of motor function as one of the main goals of rehabilitation. Modern concepts of motor learning favor a task-specific training in which all movements in daily life should be trained/assisted repetitively in a physically correct fashion. Considering the functional activity of the neuronal circuits within the spinal cord, namely the central pattern generator (CPG), as the foundation to human locomotion, motor relearning should be based on intensive training strategies directed to the stimulation and reorganization of such neural pathways through mechanisms addressed by neural plasticity. To this end, neuromodelings are required to simulate the human locomotion control to overcome the current technological challenges such as developing smaller, intelligent and cost-effective devices for home and work rehabilitation scenarios which can enable a continuous therapy/ assistance to guide the impaired limbs in a gentle manner, avoiding abrupt perturbations and providing as little assistance as necessary. Biomimetic models, taking neurological and biomechanical inspiration from biological animals, have been embracing these challenges and developing effective solutions on refining the locomotion models in terms of energy efficiency, simplicity in the structure and robust adaptability to environment changes and unexpected perturbations. Thus, the aim target of this work is to study the applicability of the CPG model for gait rehabilitation, either for assistance and/or therapy purposes. Focus is developed on the locomotion control to increase the knowledge of the underlying principles useful for gait restoration, exploring the brainstem-spinal-biomechanics interaction more fully. This study has great application in the project of autonomous robots and in the rehabilitation technology, not only in the project of prostheses and orthoses, but also in the searching of procedures that help to recuperate motor functions of human beings. Encouraging results were obtained which pave the way towards the simulation of more complex behaviors and principles of human locomotion, consequently contributing for improved automated motor rehabilitation adapted to the rehabilitation emerging needs.Actualmente a debilidade motora em países industrializados devido a deficiências neurais e físicas é um fenómeno crescente de apreensão sendo expectável um contínuo aumento do rácio de pacientes nas próximas décadas devido ao processo de envelhecimento. Inclusivé, o aumento da idade de reforma, a maior procura por parte dos idosos para uma mobilidade e vida autónoma e condigna, o elevado custo nos cuidados de saúde são incentivos para a restauração da função motora como um dos objectivos principais da reabilitação. Conceitos recentes de aprendizagem motora apoiam um treino de tarefas específicas no qual movimentos no quotidiano devem ser treinados/assistidos de forma repetitiva e fisicamente correcta. Considerando a actividade funcional dos circuitos neurais na medula, nomeadamente o gerador de padrão central (CPG), como a base da locomoção, a reaprendizagem motora deve-se basear em estratégias intensivas de treino visando a estimulação e reorganização desses vias neurais através de mecanismos abordados pela plasticidade neural. Assim, são necessários modelos neurais para simular o controlo da locomoção humana de modo a superar desafios tecnológicos actuais tais como o desenvolvimento de dispositivos mais compactos, inteligentes e económicos para os cenários de reabilitação domiciliar e laboral que podem permitir uma terapia/assistência contínua na guia dos membros debilitados de uma forma suave, evitando perturbações abruptas e fornecendo assistência na medida do necessário. Modelos biomiméticos, inspirando-se nos princípios neurológicos e biomecânicos dos animais, têm vindo a abraçar esses desafios e a desenvolver soluções eficazes na refinação de modelos de locomoção em termos da eficiência de energia, da simplicidade na estrutura e da adaptibilidade robusta face a alterações ambientais e perturbações inesperadas. Então, o objectivo principal do trabalho é estudar a aplicabilidade do modelo de CPG para a reabilitação da marcha, para efeitos de assistência e/ou terapia. É desenvolvido um foco no controlo da locomoção para maior entendimento dos princípios subjacentes úteis para a recuperação da marcha, explorando a interacção tronco cerebral-espinal medula-biomecânica de forma mais detalhada. Este estudo tem potencial aplicação no projecto de robôs autónomos e na tecnologia de reabilitação, não só no desenvolvimento de ortóteses e próteses, mas também na procura de procedimentos úteis para a recuperação da função motora. Foram obtidos resultados promissores susceptíveis de abrir caminho à simulação de comportamentos e princípios mais complexos da marcha, contribuindo consequentemente para uma aprimorada reabilitação motora automatizada adaptada às necessidades emergentes

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis