Hierarchical Compliance Control of a Soft Ankle Rehabilitation Robot Actuated by Pneumatic Muscles

Traditional compliance control of a rehabilitation robot is implemented in task space by using impedance or admittance control algorithms. The soft robot actuated by pneumatic muscle actuators (PMAs) is becoming prominent for patients as it enables the compliance being adjusted in each active link, which, however, has not been reported in the literature. This paper proposes a new compliance control method of a soft ankle rehabilitation robot that is driven by four PMAs configured in parallel to enable three degrees of freedom movement of the ankle joint. A new hierarchical compliance control structure, including a low-level compliance adjustment controller in joint space and a high-level admittance controller in task space, is designed. An adaptive compliance control paradigm is further developed by taking into account patient’s active contribution and movement ability during a previous period of time, in order to provide robot assistance only when it is necessarily required. Experiments on healthy and impaired human subjects were conducted to verify the adaptive hierarchical compliance control scheme. The results show that the robot hierarchical compliance can be online adjusted according to the participant’s assessment. The robot reduces its assistance output when participants contribute more and vice versa, thus providing a potentially feasible solution to the patient-in-loop cooperative training strateg

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

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

Towards a human-in-the-loop control for a smart orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Stroke is the main cause of paralysis. This pathology has provoked a considerable increase of persons with motor impairments. With a therapy focused on each clinical case, the total or partial recovery can be achieved. Powered orthoses have been developed to promote an effective recover, based on repetitive gait training and user’s active participation. Many control approaches have been developed to control these devices, but none of them promotes an user-oriented strategy focused to the user’s needs. In an attempt of solving this issue, a new approach named Human-in-the-loop is emerging. This strategy allows the adaptation of some assistive parameters based on the user’s energetic cost, promoting a therapy tailored to each end-user needs. However, to estimate the energy expenditure, the use of non-ergonomic sensors, not suitable for clinical context, is required. Thus, it is necessary to find new ways of estimating energy expenditure using wearable and comfortable sensors. In this dissertation, the first steps to introduce the Human-in-the-loop strategy into a powered orthosis are presented. For this purpose, two strategies were developed: a strategy that allows the angular trajectory adaptation in real-time and other that promotes a stiffness adaptation all over the gait cycle. Both strategies were validated with healthy subjects. In the first strategy, the orthosis was able to modify its assistance in a fraction of microseconds, and the end-users were able to follow her with a median error below 10%. Regarding the second strategy, the results show that the orthosis allowed an effective change in the systems’ interaction stiffness, promoting an active participation of each user during its assistance. The energetic impact of using the robotic assistive device is also presented. As it promotes an energy expenditure augmentation in more than 30% in comparison to walk without the device, the necessity of implementing the Human-in-the-loop strategy was highlighted. In an attempt of finding an ergonomic technique to estimate the energetic cost, the use of machine learning algorithms was tested. The results, obtained with a MLP and a LSTM, prove that it is possible to estimate the energy expenditure with a mean error close to 11%. Future work consists in the implementation of the model in real-time and the collection of more data with the aforementioned control approaches, in a way of constructing a more robust model.O AVC é uma das maiores causas de paralisia. Esta patologia, cada vez mais com maior incidência nos jovens, tem provocado um aumento considerável de pessoas com problemas de mobilidade. Com uma terapia focada a cada caso clínico, a recuperação total ou parcial pode ser conseguida. As ortóteses ativas têm vindo a ser desenvolvidas com o propósito de promover uma recuperação eficaz, baseada em treinos repetitivos e numa participação ativa dos utilizadores. Várias abordagens de controlo têm vindo a ser desenvolvidas para controlar estes dispositivos, mas nenhuma delas promove uma estratégia orientada às necessidades do utilizador. Na tentativa de solucionar este problema, uma nova abordagem, designada por Human-in-the-loop está a emergir. Baseada no custo energético, esta estratégia permite adaptar parâmetros da assistência, promovendo uma terapia focada e direcionada a cada utilizador. No entanto, para estimar o custo energético, recorre-se ao uso de sensores que não são adequados para contexto clínico. Assim, torna-se necessário estudar novas formas de estimar o custo energético. Nesta dissertação são apresentados os primeiros passos para introduzir o controlo Human-in-the-loop numa ortótese ativa. Para isso, duas estratégias foram apresentadas: uma estratégia que permite adaptar a trajetória angular da ortótese, em tempo real, e outra que promove a adaptação da complacência do sistema ao longo do ciclo da marcha. Ambas foram validadas com sujeitos saudáveis. Relativamente à primeira abordagem, a ortótese foi capaz de modificar a sua assistência em microssegundos, e os utilizadores foram capazes de a seguir com um erro mediano inferior a 10%. No que diz respeito à segunda abordagem, os resultados mostram que a ortótese promoveu uma alteração eficaz da complacência de interação, promovendo uma participação ativa do utilizador durante a sua assistência. O impacto energético do uso do sistema robótico é, também, apresentado. Promovendo um aumento do custo energético em mais de 30%, a necessidade da estratégia Human-in-the-loop foi realçada. Na tentativa de encontrar uma técnica para estimar o custo energético, recorreu-se ao uso de machine learning. Os resultados, obtidos com uma MLP e uma LSTM, provam que é possível estimar o custo energético com um erro médio próximo dos 11%. Trabalho futuro passa pela implementação do modelo em tempo real e a recolha de mais dados com as abordagens de controlo apresentadas, de forma a construir um modelo mais robusto

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

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

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

A multilevel framework to measure, model, promote, and enhance the symbiotic cooperation between humans and robotic devices

In the latest decades, the common perception about the role of robotic devices in the modern society dramatically changed. In the early stages of robotics, temporally located in the years of the economic boom, the development of new devices was driven by the industrial need of producing more while reducing production time and costs. The demand was, therefore, for robotic devices capable of substituting the humans in performing simple and repetitive activities. The execution of predefined basic activities in the shortest amount of time, inside carefully engineered and confined environments, was the mission of robotic devices. Beside the results obtained in the industrial sector, a progressive widening of the fields interested in robotics – such as rehabilitation, elderly care, and medicine – led to the current vision of the device role. Indeed, these challenging fields require the robot to be a partner, which works side-by-side with the human. Therefore, the device needs to be capable of actively and efficiently interacting with humans, to provide support and overcome their limits in the execution of shared activities, even in highly unpredictable everyday environments. Highly complex and advanced robots, such as surgical robots, rehabilitation devices, flexible manipulators, and service and companion robots, have been recently introduced into the market; despite their complexity, however, they are still tools to be used to perform, better or faster, very specific tasks. The current open challenge is, therefore, to develop a new generation of symbiotically cooperative robotic partners, adding to the devices the capability to detect, understand, and adapt to the real intentions, capabilities, and needs of the humans. To achieve this goal, a bidirectional information channel shall be built to connect the human and the device. In one direction, the device requires to be informed about the state of its user; in the other direction, the human needs to be informed about the state of the whole interacting system. This work reports the research activities that I conducted during my PhD studies in this research direction. Those activities led to the design, development, and assessment on a real application of an innovative multilevel framework to close the cooperation loop between a human and a robotic device, thus promoting and enhancing their symbiotic interaction. Three main levels have been identified as core elements to close this loop: the measure level, the model level, and the extract/synthesize level. The former aims at collecting experimental measures from the whole interacting system; the second aims at estimating and predicting its dynamic behavior; the last aims at providing quantitative information to both the human and the device about their performances and about how to modify their behavior to improve their interaction symbiosis. Within the measure level, the focus has been concentrated on investigating, critically comparing, and selecting the most suitable and advanced technologies to measure kinematics and dynamics quantities in a portable and minimally intrusive way. Particular attention has been paid to new emerging technologies; moreover, useful protocols and pipelines already recognized as de-facto in other fields have been successfully adapted to fit the needs of the man-machine interaction context. Finally, the design of a new sensor has been started to overcome the lack of tools capable of effectively measuring human-device interaction forces. To implement the model level, a common platform to perform integrated multilevel simulations – i.e. simulations where the device and the human are considered together as interacting entities – has been selected and extensively validated. Furthermore, critical aspects characterizing the modeling of the device, the human, and their interactions have been studied and possible solutions have been proposed. For example, modeling the mechanics and the control within the selected software platform allowed accurate estimations of their behavior. To estimate human behavior, new methodologies and approaches based on anatomical neuromusculoskeletal models have been developed, validated, and released as open-source tools for the community, to allow accurate estimates of both kinematics and dynamics at run-time – i.e. at the same time that the movements are performed. An inverse kinematics approach has been developed and validated to estimate human joint angles from the orientation measurements provided by wearable inertial systems. Additionally, a state of the art neuromusculoskeletal modeling toolbox has been improved and interfaced with the other tools of the multilevel framework, to accurately predict human muscle forces, joint moments, and muscle and joint stiffness from electromyographic and kinematic measures. To estimate and predict the interactions, contact models, parameters optimization procedures, and high-level cooperation strategies have been investigated, developed, and applied. Within the extract/synthesize level, the information provided by the other levels has been combined together to develop informative feedbacks for both the device and the human. In one direction, the device has been provided with control signals defining how to adjust the provided support to comply with the task goals and with the human current capabilities and needs. In the other direction, quantitative feedbacks have been developed to inform the human about task execution performances, task targets, and support provided by the device. This information has been provided to the user as visual feedbacks designed to be both exhaustively informative and minimally distractive, to prevent possible loss of focus. Moreover, additional feedbacks have been devised to help external observers – therapists in the rehabilitation contexts or task planners and ergonomists in the industrial field – in the design and refinement of effective personalized tasks and long-term goals. The integration of all the hardware and software tools of each level in a modular, flexible, and reliable software framework, based on a well known robotic middleware, has been fundamental to handle the communication and information exchange processes. The developed general framework has been finally specialized to face the specific needs of robotic-aided gait rehabilitation. In this context, indeed, the final aim of promoting the symbiotic cooperation is translatable in maximizing treatment effectiveness for the patients by actively supporting their changing needs and capabilities while keeping them engaged during the whole rehabilitation process. The proposed multilevel framework specialization has been successfully used, as valuable answer to those needs, within the context of the Biomot European project. It has been, indeed, fundamental to face the challenges of closing the informative loop between the user and the device, and providing valuable quantitative information to the external observers. Within this research project, we developed an innovative compliant wearable exoskeleton prototype for gait rehabilitation capable of adjusting, at run-time, the provided support according to different cooperation strategies and to user needs and capabilities. At the same time, the wearer is also engaged in the rehabilitation process by intuitive visual feedbacks about his performances in the achievement of the rehabilitation targets and about the exoskeleton support. Both researchers and clinical experts evaluating the final rehabilitation application of the multilevel framework provided enthusiastic feedbacks about the proposed solutions and the obtained results. To conclude, the modular and generic multilevel framework developed in this thesis has the potential to push forward the current state of the art in the applications where a symbiotic cooperation between robotic devices and humans is required. Indeed, it effectively endorses the development of a new generation of robotic devices capable to perform challenging cooperative tasks in highly unpredictable environments while complying with the current needs, intentions, and capabilities of the human

Real-time biped character stepping

PhD ThesisA rudimentary biped activity that is essential in interactive evirtual worlds, such as video-games and training simulations, is stepping. For example, stepping is fundamental in everyday terrestrial activities that include walking and balance recovery. Therefore an effective 3D stepping control algorithm that is computationally fast and easy to implement is extremely valuable and important to character animation research. This thesis focuses on generating real-time controllable stepping motions on-the-fly without key-framed data that are responsive and robust (e.g.,can remain upright and balanced under a variety of conditions, such as pushes and dynami- cally changing terrain). In our approach, we control the character’s direction and speed by means of varying the stepposition and duration. Our lightweight stepping model is used to create coordinated full-body motions, which produce directable steps to guide the character with specific goals (e.g., following a particular path while placing feet at viable locations). We also create protective steps in response to random disturbances (e.g., pushes). Whereby, the system automatically calculates where and when to place the foot to remedy the disruption. In conclusion, the inverted pendulum has a number of limitations that we address and resolve to produce an improved lightweight technique that provides better control and stability using approximate feature enhancements, for instance, ankle-torque and elongated-body