Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Design and development of the sEMG-based exoskeleton strength enhancer for the legs

This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed,3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are usedcoherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus,the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional liftto provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper

Design and control of a single-leg exoskeleton with gravity compensation for children with unilateral cerebral palsy

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler–Lagrange formulation and following Denavit–Hartenberg rules was derived and validated in Simscape™ and Simulink® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy

Adaptive Controllers for Assistive Robotic Devices

Lower extremity assistive robotic devices, such as exoskeletons and prostheses, have the potential to improve mobility for millions of individuals, both healthy and disabled. These devices are designed to work in conjunction with the user to enhance or replace lost functionality of a limb. Given the large variability in walking dynamics from person to person, it is still an open research question of how to optimally control such devices to maximize their benefit for each individual user. In this context, it is becoming more and more evident that there exists no "one size fits all" solution, but that each device needs to be tuned on a subject-specific basis to best account for each user's unique gait characteristics. However, the controllers that run in the background of these devices to dictate when and what type of actuation to deliver often have up to a hundred different parameters that can be tuned on a subject-specific basis. To hand tune each parameter can be an extremely tedious and time consuming process. Additionally, current tuning practices often rely on subjective measures to inform the fitting process. To address the current obstacles associated with device control and tuning, I have developed novel tools that overcome some of these issues through the design of control architectures that autonomously adapt to the user based upon real-time physiological measures. This approach frames the tuning process of a device as a real-time optimization and allows for the device to co-adapt with the wearer during use. As an outcome of these approaches, I have been able to investigate what qualities of a device controller are beneficial to users through the analysis of whole body kinematics, dynamics, and energetics. The framework of my research has been broken down into four major projects. First, I investigated how current standards of processing and analyzing physiological measures could be improved upon. Specifically, I focused on how to analyze non-steady-state measures of metabolic work rate in real time and how the noise content of theses measures can inform confidence analyses. Second, I applied the techniques I developed for analyzing non-steady-state measures of metabolic work rate to conduct a real-time optimization of powered bilateral ankle exoskeletons. For this study I employed a gradient descent optimization to tune and optimize an actuation timing parameter of these simple exoskeletons on a subject-specific basis. Third, I investigated how users may use an adaptive controller where they had a more direct impact on the adaptation via their own muscle recruitment. In this study, I designed and tested an adaptive gain proportional myoelectric controller with healthy subjects walking in bilateral ankle exoskeletons. Through this work I showed that subjects adapted to using increased levels of total ankle power compared to unpowered walking in the devices. As a result, subjects decreased power at their hip and were able to achieve large decreases in their metabolic work rate compared to unpowered walking. Fourth, I compared how subjects may use a controller driven by neural signals differently than one driven by mechanically intrinsic signals. The results of this project suggest that control based on neural signals may be better suited for therapeutic rehabilitation than control based on mechanically intrinsic signals. Together, these four projects have drastically improved upon subject-specific control of assistive devices in both a research and clinical setting.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144029/1/jrkoller_1.pd

Real-time Hybrid Locomotion Mode Recognition for Lower-limb Wearable Robots

Real-time recognition of locomotion-related activities is a fundamental skill that the controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities

Development of an exoskeleton model in a neurorehabilittion perspective

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2017A locomoção é uma tarefa de grande importância na vida das pessoas. Ainda que pareça uma tarefa simples, andar é um exercício complexo que envolve controlo nervoso a fim de ativar os músculos e criar um movimento coordenado. Embora exista variabilidade natural nos padrões de marcha de indivíduos saudáveis, é possível definir um padrão “normal”. O mínimo distúrbio a nível neuromuscular que afete a marcha de um individuo resulta na perturbação da qualidade de vida do mesmo, podendo mesmo condicionar a sua independência. Paralisia Cerebral, Esclerose Lateral Amiotrófica e Parkinson são algumas das doenças que podem afetar o padrão normal da marcha. Outra condição que pode desencadear alterações é o Acidente Vascular Cerebral (AVC), de acordo com a com a Organização Mundial de Saúde, cerca de 15 milhões de pessoas em cada ano sofrem um AVC, das quais 50% sofrem alterações da marcha não permanentes. Cada uma das condições mencionadas provoca alterações diferentes à marcha normal permitindo a definição de padrões de marcha de acordo com a condição que os afeta. Por norma, o tratamento mais utilizado para distúrbios da marcha é reabilitação motora que consiste na realização repetida de exercícios que permitem a estimulação dos músculos de forma a que voltem a estar ativos. Ao longo do tempo as técnicas de reabilitação motora foram evoluindo e recentemente a engenharia uniu-se à medicina para originar uma nova área: a Reabilitação Robótica. Esta área faz uso de tecnologias robóticas com o objetivo de proporcionar um tratamento mais personalizado e adequado a cada paciente, beneficiando assim quer o paciente, quer os terapeutas. Embora ainda esteja em crescimento, esta área tem já demonstrado um grande potencial. O Exoesqueleto é um dispositivo robótico que começou por ser usado em fins militares de forma a aumentar a capacidade que cada soldado carrega, é agora bastante utilizado na Reabilitação Robótica. Este dispositivo estimula o paciente a andar e vai apoiando conforme necessário, respondendo ao paradigma ajudar tanto quanto necessário, ou seja, o dispositivo ajuda o paciente a caminhar, dando-lhe apenas o impulso necessário para que este consiga prosseguir, tendo como objetivo final deixar de ser necessário enviar este impulso. Este procedimento é determinado pela estrutura de controlo do exosqueleto que consiste na estratégia que rege e define o comportamento do dispositivo robótico de acordo com a informação que os sensores do mesmo lhe fornecem. Por exemplo, existem controlos de posição, em que o exosqueleto conhece uma trajetória de padrão normal e ajusta a posição do paciente mediante a diferença que deteta entre a posição dita atual e a posição de referência. A estratégia de controlo desempenha também um papel muito importante no âmbito da Reabilitação Robótica, é claro que os pacientes beneficiam de terapias o mais personalizadas possível, no entanto, o desenvolvimento de uma estratégia de controlo é um processo moroso e que envolve recursos. Uma possível solução para esta limitação é a simulação, que consiste na imitação de um processo ou sistema do mundo real em função do tempo, sendo usado para processos de otimização, testes, treinos e engenharia de segurança. Tendo isto em conta, simulação seria uma forma rápida e económica de estudar novas estratégias de controlo ou até otimizar já existentes. O objetivo deste trabalho consistiu em desenvolver um modelo capaz de realizar simulações de um exosqueleto, mais especificamente do exosqueleto H1, desenvolvido ao abrigo do projeto HYPER. Este modelo foi desenvolvido em OpenSim, um simulador de uso livre desenvolvido pelo National Center for Simulation in Rehabilitation Research (NCSRR), Stanford University, USA. Este simulador é usado maioritariamente para projetos na área da biomecânica com especial enfoque para o estudo do comportamento de sistemas músculo-esqueléticos. Primeiramente, foi efetuado um estudo intensivo sobre padrões de marcha, de forma a perceber quais as condições que podem afetar a marcha de um individuo. Este estudo apresenta a definição de alguns padrões de marcha como: (1) Padrão Normal, (2) Padrão Hemiplégico, causado por AVC, (3) Padrão Diplégico, causado por Paralisia Cerebral, (4)Padrão Neuropático, causado por Esclerose Lateral Amiotrófica, (5) Padrão Miotrófico, causado por Distrofia Muscular, (6)Padrão Parkinsoniano, causado pela doença de Parkinson. Além disto, foi realizada uma pesquisa bibliográfica de forma a conhecer o estado da arte das estratégias de controlo usadas na área de Reabilitation Robótica. Conhecer as características de um padrão de marcha, bem como dos controladores existentes é importante na medida em pode ser interessante desenvolver estratégias de controlo de acordo com o padrão de marcha ou pelo menos conhecer que padrões se devem ajustar para uma terapia mais eficaz de acordo com a condição que afeta o paciente. A construção deste modelo iniciou-se no SolidWorks, um software de desenho assistido por computador, onde o sistema foi modelado de acordo com as propriedades físicas do H1, seguindo-se modelação por código em XML. Após a construção, o modelo foi validado. Para efetuar esta validação foram efetuadas provas estáticas e em movimento com o exosqueleto, tendo sido recolhidos os seguintes dados: ângulos e momento de cada articulação. Os momentos recolhidos nestas provas foram depois comparados com os momentos calculados com a ferramenta Inverse Dynamics do OpenSim, que usou como dados de entrada os ângulos de cada articulação. O modelo construído, denominado Exoskeleton, foi depois integrado num novo modelo em conjunto com um modelo já disponível na base de dados OpenSim, o 3DGait2392. A junção destes modelos deu origem ao ExoBody, um modelo que permite estudar a interação entre o dispositivo robótico e o paciente. Apesar de este modelo não ter passado por um processo de validação análogo ao do Exoskeleton, foi usado para um pequeno estudo de marcha onde se comparou a marcha de um individuo saudável com um paciente de AVC com e sem o uso do exosqueleto. Para a realização deste estudo foram utilizados data sets disponíveis online na base de dados OpenSim, estando já preparados para ser usados como dados de entrada das ferramentas Inverse Kinemaitcs e Inverse Dynamics. A Inverse Kynematics é uma ferramente que calcula para cada instante de tempo a posição do modelo que melhor corresponde à posição experimental, sendo esta determinada por marcadores por norma colocados na pele do individuo em estudo. A Inverse Dynamics, por sua vez, determina as forças generalizadas responsáveis por um determinado movimento em cada articulação. Ambos os modelos construídos são capazes de realizar simulações no OpenSim sem gerar erros de sistema e dentro de tempos computacionais considerados normais. Tal como esperado, a comparação entre os dados experimentais e os dados simulados referentes ao modelo Exoskeleton foram concordantes e por isso o modelo foi validado com sucesso. Considerando o ExoBody model, os resultados apresentados evidenciam diferenças entre os padrões de marcha e também é possível verificar diferenças aquando do uso do exosqueleto ou sem o mesmo. Posto isto, é possível concluir que os objetivos deste trabalho foram alcançados com sucesso uma vez que se desenvolveu o modelo que permite a simulação do exosqueleto bem como a sua personalização, adição de componentes como atuadores ou controladores. É importante referir que o modelo Exoskeleton tem algumas limitações, nomeadamente referentes ao design do mesmo que poderá ser melhorado. Partindo deste trabalho, novos desafios podem ser enfrentados na perspetiva de continuar a melhorar e abrir horizontes na Reabilitação Robótica, nomeadamente, seria importante fazer uma validação do ExoBody incluindo um estudo de forças de reação.Locomotion plays a very important role in a person’s life. Although healthy individuals show natural variability in gait patterns, it is possible to define an acceptable pattern for “normal gait”. However, some pathologies as Amyotrophic Lateral Sclerosis (ALS), Spinal Cord Injury (SCI), Stroke or others can induce abnormal gait patterns that can limit the life of a person, making him/her dependent of others and consequently reducing his/hers quality of life. Robotics rehabilitation therapies are a growing solution that intends to revert or diminish the impairments in gait. The use of robotic devices, such as exoskeletons, cover some limitations of the traditional therapeutic methods, which is a great benefit for both patients and therapists. Furthermore, the application of an adequate treatment in these patients can be improved with the understanding of how the pathology affects the individual and through the development of specific solutions for each patient. Nowadays, computational dynamic simulations have great potential and help researchers to find optimal and personalized solutions for each patient. Thus, the present work describes the development of an exoskeleton model in a neurorehabilitation perspective. First of all, a detailed description of gait patterns is presented, followed by the state of the art in robotics rehabilitation, considering that this field contains very powerful solutions for gait disorders. The model was developed in OpenSim, an open source software dedicated to model musculoskeletal systems and dynamic simulations of movement. In order to verify the accuracy of the model, experimental data were collected in static and motion trials performed with the wearable robot and afterwards compared with the simulated data resultant from Inverse Dynamics, a tool from OpenSim. The Exoskeleton model was successfully validated and then integrated in a new model, named ExoBody, within a musculoskeletal model. The ExoBody model was used to perform gait analysis comparing simulations with and without the exoskeleton, revealing some differences. Even though the built models present limitations, this work represents a step-forward in human-centered rehabilitation

Development of Digital Control Systems for Wearable Mechatronic Devices: Applications in Musculoskeletal Rehabilitation of the Upper Limb

The potential for wearable mechatronic systems to assist with musculoskeletal rehabilitation of the upper limb has grown with the technology. One limiting factor to realizing the benefits of these devices as motion therapy tools is within the development of digital control solutions. Despite many device prototypes and research efforts in the surrounding fields, there are a lack of requirements, details, assessments, and comparisons of control system characteristics, components, and architectures in the literature. Pairing this with the complexity of humans, the devices, and their interactions makes it a difficult task for control system developers to determine the best solution for their desired applications. The objective of this thesis is to develop, evaluate, and compare control system solutions that are capable of tracking motion through the control of wearable mechatronic devices. Due to the immaturity of these devices, the design, implementation, and testing processes for the control systems is not well established. In order to improve the efficiency and effectiveness of these processes, control system development and evaluation tools have been proposed. The Wearable Mechatronics-Enabled Control Software framework was developed to enable the implementation and comparison of different control software solutions presented in the literature. This framework reduces the amount of restructuring and modification required to complete these development tasks. An integration testing protocol was developed to isolate different aspects of the control systems during testing. A metric suite is proposed that expands on the existing literature and allows for the measurement of more control characteristics. Together, these tools were used ii ABSTRACT iii to developed, evaluate, and compare control system solutions. Using the developed control systems, a series of experiments were performed that involved tracking elbow motion using wearable mechatronic elbow devices. The accuracy and repeatability of the motion tracking performances, the adaptability of the control models, and the resource utilization of the digital systems were measured during these experiments. Statistical analysis was performed on these metrics to compare between experimental factors. The results of the tracking performances show some of the highest accuracies for elbow motion tracking with these devices. The statistical analysis revealed many factors that significantly impact the tracking performance, such as visual feedback, motion training, constrained motion, motion models, motion inputs, actuation components, and control outputs. Furthermore, the completion of the experiments resulted in three first-time studies, such as the comparison of muscle activation models and the quantification of control system task timing and data storage needs. The successes of these experiments highlight that accurate motion tracking, using biological signals of the user, is possible, but that many more efforts are needed to obtain control solutions that are robust to variations in the motion and characteristics of the user. To guide the future development of these control systems, a national survey was conducted of therapists regarding their patient data collection and analysis methods. From the results of this survey, a series of requirements for software systems, that allow therapists to interact with the control systems of these devices, were collected. Increasing the participation of therapists in the development processes of wearable assistive devices will help to produce better requirements for developers. This will allow the customization of control systems for specific therapies and patient characteristics, which will increase the benefit and adoption rate of these devices within musculoskeletal rehabilitation programs

EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It i