194 research outputs found

    Assisting walking balance using a bio-inspired exoskeleton controller

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    Background: Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity. Methods: Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton. Results: Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p < 0.001). Proportional feedback of the center of mass velocity improved balance support after perturbation. Muscle activity is reduced in response to push and pull perturbations by 10% (p = 0.006) and 16% (p < 0.001) and center of mass deviations by 9% (p = 0.026) and 18% (p = 0.002) with respect to the same controller without center of mass feedback. Conclusion: Our control approach implemented on bilateral ankle exoskeletons can thus effectively support steady-state walking and balance control and therefore has the potential to improve mobility in balance-impaired individuals.Support Biomechanical Engineerin

    Neuromechanical Model-Based Adaptive Control of Bilateral Ankle Exoskeletons:Biological Joint Torque and Electromyogram Reduction Across Walking Conditions

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    To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. We call this 'neuromechanical model-based control' (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque;12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as an exomuscle controlled by the operator.s neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings

    Otimização muscle-in-the-loop em tempo real para reabilitação física com um exosqueleto ativo: uma mudança de paradigma

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    Assisting human locomotion with a wearable robotic orthosis is still quite challenging, largely due to the complexity of the neuromusculoskeletal system, the time-varying dynamics that accompany motor adaptation, and the uniqueness of every individual’s response to the assistance given by the robot. To this day, these devices have not met their well-known promise yet, mostly due to the fact that they are not perfectly suitable for the rehabilitation of neuropathologic patients. One of the main challenges hampering this goal still relies on the interface and co-dependency between the human and the machine. Nowadays, most commercial exoskeletons replay pre-defined gait patterns, whereas research exoskeletons are switching to controllers based on optimized torque profiles. In most cases, the dynamics of the human musculoskeletal system are still ignored and do not take into account the optimal conditions for inducing a positive modulation of neuromuscular activity. This is because both rehabilitation strategies are still emphasized on the macro level of the whole joint instead of focusing on the muscles’ dynamics and activity, which are the actual anatomical elements that may need to be rehabilitated. Strategies to keep the human in the loop of the exoskeleton’s control laws in real-time may help to overcome these challenges. The main purpose of the present dissertation is to make a paradigm shift in the approach on how the assistance that is given to a subject by an exoskeleton is modelled and controlled during physical rehabilitation. Therefore, in the scope of the present work, it was intended to design, concede, implement, and validate a real-time muscle-in-the-loop optimization model to find the best assistive support ratio that would induce optimal rehabilitation conditions to a specific group of impaired muscles while having a minimum impact on the other healthy muscles. The developed optimization model was implemented in the form of a plugin and was integrated on a neuromechanical model-based interface for driving a bilateral ankle exoskeleton. Experimental pilot tests evaluated the feasibility and effectiveness of the model. Results of the most significant pilots achieved EMG reductions up to 61 ± 3 % in Soleus and 41 ± 10 % in Gastrocnemius Lateralis. Moreover, results also demonstrated the efficiency of the optimization’s specific reduction on rehabilitation by looking into the muscular fatigue after each experiment. Finally, two parallel preliminary studies emerged from the pilots, which looked at muscle adaptation, after a new assistive condition had been applied, over time and at the effect of the lateral positioning of the exoskeleton’s actuators on the leg muscles.Auxiliar a locomoção humana com uma ortose robótica ainda é bastante desafiante, em grande parte devido à complexidade do sistema neuromusculoesquelético, à dinâmica variável no tempo que acompanha a adaptação motora e à singularidade da resposta de cada indivíduo à assistência dada pelo robô. Até hoje, está por cumprir a promessa inicial destes dispositivos, principalmente devido ao facto de não serem perfeitamente adequados para a reabilitação de pacientes neuropatológicos. Um dos principais desafios que dificultam esse objetivo foca-se ainda na interface e na co-dependência entre o ser humano e a máquina. Hoje em dia, a maioria dos exoesqueletos comerciais reproduz padrões de marcha predefinidos, enquanto que os exoesqueletos em investigação estão só agora a mudar para controladores com base em perfis de binário otimizados. Na maioria dos casos, a dinâmica do sistema musculoesquelético humano ainda é ignorada e não tem em consideração as condições ideais para induzir uma modulação positiva da atividade neuromuscular. Isso ocorre porque ambas as estratégias de reabilitação ainda são enfatizadas no nível macro de toda a articulação, em vez de se concentrar na dinâmica e atividade dos músculos, que são os elementos anatómicos que realmente precisam de ser reabilitados. Estratégias para manter o ser humano em loop nos comandos que controlam o exoesqueleto em tempo real podem ajudar a superar estes desafios. O principal objetivo desta dissertação é fazer uma mudança de paradigma na abordagem em como a assistência que é dada a um sujeito por um exosqueleto é modelada e controlada durante a reabilitação física. Portanto, no contexto do presente trabalho, pretendeu-se projetar, conceder, implementar e validar um modelo de otimização muscle-in-the-loop em tempo real para encontrar a melhor relação de suporte capaz de induzir as condições ideais de reabilitação para um grupo específico de músculos fragilizados, tendo um impacto mínimo nos outros músculos saudáveis. O modelo de otimização desenvolvido foi implementado na forma de um plugin e foi integrado numa interface baseada num modelo neuromecânico para o controlo de um exoesqueleto bilateral de tornozelo. Testes experimentais piloto avaliaram a viabilidade e a eficácia do modelo. Os resultados dos testes mais significativos demonstraram reduções de EMG de até 61 ± 3 % no Soleus e 41 ± 10 % no Gastrocnemius Lateral. Adicionalmente, os resultados demonstraram também a eficiência em reabilitação da redução específica no EMG devido à otimização tendo em conta a fadiga muscular após cada teste. Finalmente, dois estudos preliminares paralelos emergiram dos testes piloto, que analisaram a adaptação muscular após uma nova condição assistiva ter sido definida ao longo do tempo e o efeito do posicionamento lateral dos atuadores do exoesqueleto nos músculos da perna.Mestrado em Engenharia Biomédic

    Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

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    Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting data on hardware can be expensive. This has led to a recent interest in adapting data-efficient learning techniques to robotics. One popular method is Bayesian Optimization (BO), a sample-efficient black-box optimization scheme, but its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge to reduce dimensionality in a meaningful way, with a focus on bipedal locomotion. In previous work, we proposed a transformation based on knowledge of human walking that projected a 16-dimensional controller to a 1-dimensional space. In simulation, this showed enhanced sample efficiency when optimizing human-inspired neuromuscular walking controllers on a humanoid model. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation and on hardware. We present three different walking controllers; two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.Comment: 8 pages, submitted to IEEE International Conference on Robotics and Automation 201

    Dynamic Simulation and Neuromechanical Coordination of Subject-Specific Balance Recovery to Prevent Falls

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    Falls are the leading cause of fatal and nonfatal injuries in elderly people, resulting in approximately $31 billion in medical costs annually in the U.S. These injuries motivate balance control studies focused on improving stability by identifying prevention strategies for reducing the number of fall events. Experiments provide data about subjects’ kinematic response to loss of balance. However, simulations offer additional insights, and may be used to make predictions about functional outcomes of interventions. Several approaches already exist in biomechanics research to generate accurate models on a subject-by-subject basis. However, these representations typically lack models of the central nervous system, which provides essential feedback that humans use to make decisions and alter movements. Interdisciplinary methods that merge biomechanics with other fields of study may be the solution to fill this gap by developing models that accurately reflect human neuromechanics.Roboticists have developed control systems approaches for humanoid robots simultaneously accomplishing complex goals by coordinating component tasks under priority constraints. Concepts such as the zero-moment point and extrapolated center of mass have been thoroughly evaluated and are commonly used in the design and execution of dynamic robotic systems in order to maintain stability. These established techniques can benefit biomechanical simulations by replacing biological sensory feedback that is unavailable in the virtual environment. Subject-specific simulations can be generated by synthesizing techniques from both robotics and biomechanics and by creating comprehensive models of task-level coordination, including neurofeedback, of movement patterns from experimental data. In this work, we demonstrate how models built on robotic principles that emulate decision making in response to feedback can be trained by biomechanical motion capture data to produce a subject-specific fit. The resulting surrogate can predict a subject’s particular solution to accomplishing the movement goal of recovering balance by controlling component tasks. This research advances biomechanics simulations as we move closer towards the development of a tool capable of anticipating the results of rehabilitation interventions aimed at correcting movement disorders. The novel platform presented here marks the first step towards that goal, and may benefit engineers, researchers, and clinicians interested in balance control and falls in human subjects

    The Associations Between Motor Corticospinal Excitability and Neuromechanics of the Paretic Soleus and Tibialis Anterior in People Post-Stroke

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    The corticospinal drive to the paretic soleus (SOL) and tibialis anterior (TA) is degraded, but whether it changes and is task-dependent remains unclear. We examined the relationships between corticospinal drive and muscle-specific neuromechanics. We collected eight measures of corticospinal drive to SOL and TA in healthy and stroke participants, and muscle-specific neuromechanics during walking and isolated task in stroke participants. We examined the reliability, and the inter-group differences in variance and mean for each corticospinal measure, and the correlations between corticospinal drive and neuromechanics of each muscle in both tasks. Only certain corticospinal measures were simultaneously reliable and had inter-group differences in variance and mean. SOL resting latency was not associated with any neuromechanical measure in either task, whereas TA resting and active latencies were associated with only the ankle angular velocity during walking. In conclusion, TA latencies may strongly indicate an impaired mechanical pattern in the ankle during walking

    Evidence for a Time-Invariant Phase Variable in Human Ankle Control

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    Human locomotion is a rhythmic task in which patterns of muscle activity are modulated by state-dependent feedback to accommodate perturbations. Two popular theories have been proposed for the underlying embodiment of phase in the human pattern generator: a time-dependent internal representation or a time-invariant feedback representation (i.e., reflex mechanisms). In either case the neuromuscular system must update or represent the phase of locomotor patterns based on the system state, which can include measurements of hundreds of variables. However, a much simpler representation of phase has emerged in recent designs for legged robots, which control joint patterns as functions of a single monotonic mechanical variable, termed a phase variable. We propose that human joint patterns may similarly depend on a physical phase variable, specifically the heel-to-toe movement of the Center of Pressure under the foot. We found that when the ankle is unexpectedly rotated to a position it would have encountered later in the step, the Center of Pressure also shifts forward to the corresponding later position, and the remaining portion of the gait pattern ensues. This phase shift suggests that the progression of the stance ankle is controlled by a biomechanical phase variable, motivating future investigations of phase variables in human locomotor control.United States Army Medical Research Acquisition Activity (USAMRAA grant W81XWH-09-2-0020)National Institute of Neurological Disorders and Stroke (U.S.) (NIH award number F31NS074687)Burroughs Wellcome Fund (Career Award at the Scientific Interface
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