Um novo modelo de conceito para implantes ortopédicos instrumentados ativos

Doutoramento em Engenharia MecânicaTotal hip replacement (THR) is one of the most performed surgical procedures around the world. Millions of THR are carried out worldwide each year. Currently, THR revision rates can be higher than 10%. A significant increase of the number of primary and revision THRs, mainly among patients less than 65 years old (including those under 45 years old) has been predicted for the forthcoming years. A worldwide increase in the use of uncemented fixation has also been reported, incidence caused mainly by the significant increase of more active and/or younger patients. Besides the significant breakthroughs for uncemented fixations, they have not been able to ensure long-term implant survival. Up to date, current implant models have shown evidences of their inability to avoid revision procedures. The performance of implants will be optimized if they are designed to perform an effective control over the osseointegration process. To pursue this goal, improved surgical techniques and rehabilitation protocols, innovative bioactive coatings (including those for controlled delivery of drugs and/or other bio-agents in the bone-implant interface), the concepts of Passive Instrumented Implant and Active Instrumented Implant have been proposed. However, there are no conclusive demonstrations of the effectiveness of such methodologies. The main goal of this thesis is to propose a new concept model for instrumented implants to optimize the bone-implant integration: the self-powered instrumented active implant with ability to deliver controlled and personalized biophysical stimuli to target tissue areas. The need of such a new model is demonstrated by optimality analyses conducted to study the performance of instrumented and non-instrumented orthopaedic implants. Promising results on the potential of a therapeutic actuation driven by cosurface-based capacitive stimulation were achieved, as well as for self-powering instrumented active implants by magnetic levitation-based electromagnetic energy harvesting.A artroplastia total da anca (THR) é um dos procedimentos cirúrgicos mais realizados à escala global. Milhões de THRs são realizadas todos os anos em todo o mundo. Atualmente, as taxas de revisão destas artroplastias podem ser superiores a 10%. O número de THRs primárias e de revisão têm aumentado e estima-se que cresçam acentuadamente nos próximos anos, principalmente em pacientes com idades inferiores a 65 anos (incluindo aqueles com menos de 45 anos). Também se tem verificado uma tendência generalizada para o uso de fixações não cimentadas, incidência principalmente causada pelo aumento significativo de pacientes mais jovens e/ou activos. Embora se tenham realizado avanços científicos no projeto de implantes não cimentados, têm-se verificado o seu insucesso a longo-prazo. Encontram-se evidências da ineficácia dos modelos de implantes que têm sido desenvolvidos para evitar procedimentos de revisão. O desempenho dos implantes será otimizado se estes foram projetados para controlarem eficazmente o processo de osseointegração. Para se alcançar este objetivo, têm sido propostas a melhoria das técnicas cirúrgicas e dos protocolos de reabilitação, a inovação dos revestimentos (onde se incluem os revestimentos ativos projetados para a libertação controlada de fármacos e/ou outros bio-agentes) e os conceitos de Implante Instrumentado Passivo e Implante Instrumentado Ativo. Contudo, não existem demonstrações conclusivas da eficácia de tais metodologias. O principal objetivo desta tese é propor um novo modelo de conceito para implantes instrumentados para se otimizar a integração osso-implante: o implante instrumentado ativo, energeticamente auto-suficiente, com capacidade de aplicar estímulos biofísicos em tecidos-alvo de forma controlada e personalizada. A necessidade de um novo modelo é demonstrada através da realização de análises de otimalidade ao desempenho dos implantes instrumentados e não-instrumentados. Foram encontrados resultados promissores para o controlo otimizado da osseointegração usando este novo modelo, através da atuação terapêutica baseada na estimulação capacitiva com arquitetura em co-superfície, assim como para fornecer energia elétrica de forma autónoma por mecanismos de transdução baseados em indução eletromagnética usando configurações baseadas na levitação magnética

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance

A Study on Active/Passive Pneumatic Actuators for Assistive Systems

The need for intelligent assistive devices is growing. Due to advances in medicine, people are living longer and able to recover from severe neurological incidents, resulting in an increased population with neuromuscular weakness. In workplaces such as assembly lines, there is a high possibility of work-related fatigue or injury, such as when workers squat down or lift their arms during their work tasks. Assistive devices could help remedy loss of strength on their extremities as well as keep the work environment safe and productive, allowing these growing segments of the population in need of the devices to live more self-sufficient, productive, and higher-quality lives.In the design of assistive systems, an important design goal is prolonged operational time, which requires the minimum usage of energy. Energy consumption can be reduced by modifying the mechanical characteristics of assistive systems according to the dynamic characteristics of the human body, which vary considerably between tasks. This dissertation investigates 1) the design of actuators with adjustable mechanical impedance, 2) control strategies to search for, and adjust to, a suitable mechanical impedance for assistance and 3) sensing technologies for classifying the tasks in which the human engages.The first part of this dissertation characterizes a pneumatic variable stiffness actuator named an Active/Passive Pneumatic Actuator (AP2A). The actuator consists of an air cylinder and an array of solenoid valves. These valves and the corresponding switching algorithms tune the chamber pressures and make the AP2A function as a mechanical spring with desired stiffness. The actuator has a low mechanical impedance compared to geared motors, which enables it to achieve efficient interaction. Control strategies of an assistive system with the AP2A are discussed in the second part. This control framework utilizes the characteristics of the AP2A to provide assistance when necessary and to operate transparently (i.e., neither to assist nor to disturb the users) otherwise. Energy consumed by the AP2A and the assisted system is minimized by solving an optimal control problem. Finally, an estimator is introduced to detect assistive timing for the assistive system with the AP2A. This estimator utilizes physiological signals such as surface electromyogram and prior knowledge of a muscular model, classifying if the user is under the specified condition to be assisted by the AP2A. It demonstrates that the user's effort can be saved, also reducing the number of procedures to collect training data for the estimator before using assistive systems. The performance of the actuator, the controller, and the estimator proposed in this dissertation are verified through experiments.From the above, this dissertation contributes to developing the AP2A that provides assistance and saves energy usage of assistive systems by working as a mechanical spring with stiffness optimized for achieving effective interaction under specific conditions. This actuator supports assistive devices that can be deployed in the real world, properly assisting the users when needed

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Enhanced pre-clinical assessment of total knee replacement using computational modelling with experimental corroboration & probabilistic applications

Demand for Total Knee Replacement (TKR) surgery is high and rising; not just in numbers of procedures, but in the diversity of patient demographics and increase of expectations. Accordingly, greater efforts are being invested into the pre-clinical analysis of TKR designs, to improve their performance in-vivo. A wide range of experimental and computational methods are used to analyse TKR performance pre-clinically. However, direct validation of these methods and models is invariably limited by the restrictions and challenges of clinical assessment, and confounded by the high variability of results seen in-vivo.Consequently, the need exists to achieve greater synergy between different pre-clinical analysis methods. By demonstrating robust corroboration between in-silico and in-vitro testing, and both identifying & quantifying the key sources of uncertainty, greater confidence can be placed in these assessment tools. This thesis charts the development of a new generation of fast computational models for TKR test platforms, with closer collaboration with in-vitro test experts (and consequently more rigorous corroboration with experimental methods) than previously.Beginning with basic tibiofemoral simulations, the complexity of the models was progressively increased, to include in-silico wear prediction, patellofemoral & full lower limb models, rig controller-emulation, and accurate system dynamics. At each stage, the models were compared extensively with data from the literature and experimental tests results generated specifically for corroboration purposes.It is demonstrated that when used in conjunction with, and complementary to, the corresponding experimental work, these higher-integrity in-silico platforms can greatly enrich the range and quality of pre-clinical data available for decision-making in the design process, as well as understanding of the experimental platform dynamics. Further, these models are employed within a probabilistic framework to provide a statistically-quantified assessment of the input factors most influential to variability in the mechanical outcomes of TKR testing. This gives designers a much richer holistic visibility of the true system behaviour than extant 'deterministic' simulation approaches (both computational and experimental).By demonstrating the value of better corroboration and the benefit of stochastic approaches, the methods used here lay the groundwork for future advances in pre-clinical assessment of TKR. These fast, inexpensive models can complement existing approaches, and augment the information available for making better design decisions prior to clinical trials, accelerating the design process, and ultimately leading to improved TKR delivery in-vivo to meet future demands

A Lower Limb Prosthesis with Active Alignment for Reduced Limb Loading

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