Effects of a neuromuscular controller on a powered ankle exoskeleton during human walking

Wearable devices to assist abnormal gaits require controllers that interact with the user in an intuitive and unobtrusive manner. To design such a controller, we investigated a bio-inspired walking controller for orthoses and prostheses. We present (i) a Simulink neuromuscular control library derived from a computational model of reflexive neuromuscular control of human gait with a central pattern generator (CPG) extension, (ii) an ankle reflex controller for the Achilles exoskeleton derived from the library, and (iii) the mechanics and energetics of healthy subjects walking with an actuated ankle orthosis using the proposed controller. As this controller was designed to mimic human reflex patterns during locomotion, we hypothesize that walking with this controller would lead to lower energetic costs, compared to walking with the added mass of the device only, and allow for walking at different speeds without explicit control. Preliminary results suggest that the neuromuscular controller does not disturb walking dynamics in both slow and normal walking cases and can also reduce the net metabolic cost compared to transparent mode of the device. Reductions in tibialis anterior and soleus activity were observed, suggesting the controller could be suitable, in future work, for augmenting or replacing normal walking functions. We also investigated the impedance patterns generated by the neuromuscular controller. The validity of the equivalent variable impedance controller, particularly in stance phase, can facilitate serving subject-specific features by linking impedance measurement and neuromuscular 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

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

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Fast biped walking with a neuronal controller and physical computation

Biped walking remains a difficult problem and robot models can greatly {facilitate} our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network, which is governed mainly by local sensor signals. This study shows that human-like gaits emerge without {specific} position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (AEA=Anterior Extreme Angle and GC=Ground Contact) which operate at the inter-joint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motorneurons in our reflexive controller are directly driving the motors of the joints, rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuro-mechanical system and this study emphasises that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using two real robot as well as by a Poincar\'{e} map analysis applied on a model of the robot in order to assess its stability. In addition, this neuronal control structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the neurons in real-time, during walking. This way the robot can reach a record-breaking walking speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is even comparable to the fastest relative speed of human walking

Fall prevention strategy for an active orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)Todos os anos, são reportadas cerca de 684,000 quedas fatais e 37.3 milhões de quedas não fatais que requerem atenção médica, afetando principalmente a população idosa. Assim, é necessário identificar eficientemente indivíduos com alto risco de queda, a partir da população alvo idosa, e prepará los para superar perturbações da marcha inesperadas. Uma estratégia de prevenção de queda capaz de eficientemente e atempadamente detetar e contrariar os eventos de perdas de equilíbrio (PDE) mais frequentes pode reduzir o risco de queda. Como slips foram identificados como a causa mais prevalente de quedas, estes eventos devem ser abordados como foco principal da estratégia. No entanto, há falta de estratégias de prevenção de quedas por slip. Esta dissertação tem como objetivo o design de uma estratégia de prevenção de quedas de slips baseada na conceção das etapas de atuação e deteção. A estratégia de atuação foi delineada com base na resposta biomecânica humana a slips, onde o joelho da perna perturbada (leading) apresenta um papel proeminente para contrariar LOBs induzidas por slips. Quando uma slip é detetada, a estratégia destaca uma ortótese de joelho que providencia um torque assisstivo para prevenir a queda. A estratégia de deteção considerou as propriedades atrativas dos controladores Central Pattern Generator (CPG) para prever parâmetros da marcha. Algoritmos baseados em threshold monitorizam o erro de previsão do CPG, que aumenta após uma perturbação inesperada na marcha, para a deteção de slips. O ângulo do joelho e a velocidade angular da canela foram selecionados como os parâmetros de monitorização da marcha. Um protocolo experimental concebido para provocar perturbações de slip a sujeitos humanos permitiu a recolha de dados destas variáveis para posteriormente validar o algoritmo de deteção de perturbações. Algoritmos CPG foram capazes de produzir aproximações aceitáveis dos sinais de marcha em estado estacionário do ângulo do joelho e da velocidade angular da canela com sucesso. Além disso, o algoritmo de threshold adaptativo detetou LOBs induzidas por slips eficientemente. A melhor performance global foi obtida usando este algoritmo para monitorizar o ângulo do joelho, que detetou quase 80% (78.261%) do total de perturbações com um tempo médio de deteção (TMD) de 250 ms. Além disso, uma média de 0.652 falsas perturbações foram detetadas por cada perturbação corretamente identificada. Estes resultados sugerem uma performance aceitável de deteção de perturbações do algoritmo, de acordo com os requisitos especificados para a deteção.Every year, an estimated 684,000 fatal falls and 37.3 million non-fatal falls requiring medical attention are reported, mostly affecting the older population. Thus, it is necessary to effectively screen high fall risk individuals from targeted elderly populations and prepare them to successfully overcome unexpected gait perturbations. A fall prevention strategy capable of effectively and timely detect and counteract the most frequent loss of balance (LOB) events may reduce the fall risk. Since slips were identified as the main contributors to falls, these events should be addressed as a main focus of the strategy. Nonetheless, there is a lack of slip-induced fall prevention strategies. This dissertation aims the design of a slip-related fall prevention strategy based on the conception of an actuation and a detection stage. The actuation strategy was delineated based on the human biomechanical reactions to slips, where the perturbed (leading) leg’s knee joint presents a prominent role to counteract slip-induced LOBs. Thereby, upon the detection of a slip, this strategy highlighted a knee orthotic device that provides an assistive torque to prevent the falls. The detection strategy considered the attractive properties of biological-inspired Central Pattern Generator (CPG) controllers to predict gait parameters. Threshold-based algorithms monitored the CPG’s prediction error produced, which increases upon an unexpected gait perturbation, to perform slip detection. The knee angle and shank angular velocity were selected as the monitoring gait parameters. An experimental protocol designed to provoke slip perturbations to human subjects allowed to collect data from these variables to further validate the perturbation detection algorithm. CPG algorithms were able to successfully produce acceptable estimations of the knee angle and shank angular velocity signals during steady-state walking. Furthermore, an adaptive threshold algorithm effectively detected slip-induced LOBs. The best overall performance was obtained using this algorithm to monitor the knee angle from the perturbed leg, which detected almost 80% (78.261%) of the total perturbations with a mean detection time (MDT) of 250 ms. In addition, a mean of 0.652 false perturbations were detected for each correct perturbation identified. These results suggest an acceptable perturbation detection performance of the algorithm implemented in light of the detection requirements specified

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

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others