This paper presents a novel approach to the design of a motorized rehabilitation device – active elbow orthosis (AEO) – inspired by the principles of robotic exoskeletons. The device is currently designed for the elbow joint, but can be easily modified for other joints as well. AEO determines the motion activity of the patient using a strain gauge and utilizes this measurement to control the actuator that drives the forearm part of the orthosis. Patient activity level is related to a free arm measurement obtained via a calibration procedure prior to the exercise. A high-level control module offers several types of exercises mimicking the physiotherapist. The device was successfully verified by tests on a number of patients, resulting in extended range of elbow-joint motion.

The main objective of my research is to improve the control structure for the new Wheelchair Mounted Robotic Arm (WMRA) to include new algorithms for optimized task execution; that is, making the WMRA a modular task oriented mobile manipulator. The main criterion to be optimized is the fashion in which the wheelchair approaches a final target as well as the starting and final orientation of the wheelchair. This is a novel approach in non-holonomic wheeled manipulators that will help in autonomously executing complex activities of daily living (ADL) tasks. The WMRA is a 9 degree of freedom system, which provides 3 degrees of kinematic redundancy. A single control structure is used to control the WMRA system, which gives much more flexibility to the system. The combination of mobility and manipulation expands the workspace that a mobile base attains to a manipulator. This approach opens a broad field of applications: from maintenance and storage to rehabilitation robotics. This structure is based on optimization algorithms that can resolve redundancy based on several subtasks: maximizing the manipulability measure, minimizing the joint velocities (hence minimizing the energy), and avoiding joint limits. This work utilizes redundancy to control 2 separate trajectories, a primary trajectory for the end-effector and an optimized secondary trajectory for the wheelchair. Even though this work presents results and implementation in the WMRA system, this approach offers expandability to many wheeled base mobile manipulators in different types of applications. The WMRA usage was simulated in a virtual environment, by developing a test setting for sensors and task performance. The different trajectories and tasks can be shown in a virtual world created not only for illustration purposes, but to provide training to the users once the system is ready for use.

Robotics in rehabilitation provides considerable opportunities to improve the quality of life for persons with disabilities. Computerized and Virtual Environment (VE) training systems for persons with disabilities, many of which utilize the haptic feedback, have gained increasing acceptance in the recent years. Our methodology here is based on creating virtual environments connected to a haptic interface as an input device. This robotic setup introduces the advantages of the haptic rendering features in the environment and also provides tactile feedback to the patients. This thesis aims to demonstrate the efficacy of assistance function algorithms in rehabilitation robotics in virtual environments. Assist functions are used to map limited human input to motions required to perform complex tasks. The purpose is to train individuals in task-oriented applications to insure that they can be incorporated into the workplace. Further, Hidden Markov Model (HMM) based motion recognition and skill learning are used for improving the skill levels of the users. For the Hidden Markov Model based motion recognition, the user's motion intention is combined with environment information to apply an appropriate assistance function. We used this algorithm to perform a commonly used vocational therapy test referred to as the box and the blocks test. The Hidden Markov Model based skill approach can be used for learning human skill and transferring the skill to persons with disabilities. A relatively complex task of moving along a labyrinth is chosen as the task to be modeled by HMM. This kind of training allows a person with disability to learn the skill and improve it through practice. Its application to motion therapy system using a haptic interface helps in improving their motion control capabilities, tremor reduction and upper limb coordination. The results obtained from all the tests demonstrated that various forms of assistance provided reduced the execution times and increased the motion performance in chosen tasks. Two persons with disabilities volunteered to perform the above tasks and both of the disabled subjects expressed an interest and satisfaction with the philosophy behind these concepts.

Le nombre croissant de personnes âgées dans le monde exige de relever de nouveaux défis sociétaux, notamment en termes de services d'aide et de soins de santé. Avec les récents progrès technologiques, la robotique apparaît comme une solution prometteuse pour développer des systèmes visant à faciliter et améliorer les conditions de vie de cette population. Cette thèse vise la proposition et la validation d'une approche de commande robuste et référencée intention d'une orthèse active, destinée à assister des mouvements de flexion/extension du genou pour des personnes souffrant de pathologies de cette articulation. La commande par modes glissants d'ordre 2 que nous proposons permet de prendre en compte les non-linéarités ainsi que les incertitudes paramétriques résultant de la dynamique du système équivalent orthèse-membre inférieur. Elle permet également de garantir d'une part, un bon suivi de la trajectoire désirée imposée par le thérapeute ou par le sujet lui-même, et d'autre part, une bonne robustesse vis-à-vis des perturbations externes pouvant se produire lors des mouvements de flexion/extension. Dans cette thèse, nous proposons également un modèle neuronal de type Perceptron Multi-Couches pour l'estimation de l'intention du sujet à partir de la mesure des signaux EMG caractérisant les activités musculaires volontaires du groupe musculaire quadriceps. Cette approche permet de s'affranchir d'un modèle d'activation et de contraction musculaire complexe. L'ensemble des travaux a été validé expérimentalement avec la participation volontaire de plusieurs sujets valides / The increasing number of elderly in the world reveals today new societal challenges, particularly in terms of healthcare and assistance services. With recent advances in technology, robotics appears as a promising solution to develop systems that improve the living conditions of this aging population. This thesis aims at proposing and validating an approach for robust control of an active orthosis, based on the subject intention. This orthosis is designed to assist flexion/ extension movements of the knee for people suffering from knee joint deficiencies. The proposed second order sliding mode control allows to take into account the nonlinearities and parametric uncertainties resulting from the dynamics of the equivalent lower limb-orthosis system. It also ensures on one hand, a good tracking performance of the desired trajectory imposed by the therapist or the subject itself, and on the second hand, a satisfactory robustness with respect to external disturbances that may occur during flexion and extension of the knee joint. In this thesis, a neural model based on Multi-Layer Perceptron is used to estimate the subject's intention from the measurement of the EMG signals characterizing the voluntary activities of the quadriceps muscle group. This approach overcomes the complex modeling of the muscular activation and contraction dynamics. All the proposed approaches in this thesis have been validated experimentally with the voluntary participation of several healthy subjects

Robôs de reabilitação como próteses ativas e exoesqueletos necessitam de atuadores capazes de atender certos requisitos como baixa impedância de saída, backdrivability, geração de torques grandes e precisos, e uma estrutura leve e compacta. Este trabalho apresenta o projeto de um Atuador Elástico em Série rotacional (AESr) para ser usado em uma prótese ativa para auxiliar na flexão/extensão da articulação do joelho durante a fisioterapia. O dispositivo é constituído de um motor de corrente contínua, um redutor de velocidade do tipo coroa e rosca sem-fim e uma mola torcional personalizada. Uma vez que o elemento elástico é o componente mais importante no projeto do AESr, um procedimento de análise baseado no Método dos Elementos Finitos (MEF) é utilizado para cumprir os requisitos definidos para a reabilitação do joelho. Com uma massa total de 2,53 Kg, é possível montar diretamente o atuador proposto em uma estrutura de prótese de joelho. Controladores de torque e impedância são implementados para assegurar uma interação segura com o paciente, permitindo que novas estratégias de reabilitação sejam avaliadas. As especificações do projeto bem como o desempenho dos controladores são validados experimentalmente. / Wearable robots, like prostheses, active orthosis and exoskeletons need of actuators able to meet certain requirements as low output impedance, backdrivability, precise and large torque generation, and a compact and lightweight design. This work presents the design of a rotary Series Elastic Actuator (rSEA) to be used in an active orthosis to assist in flexion/extension of the knee joint during physical therapy. The device includes a DC motor, a worm gear and a customized torsion spring. Since the elastic element is the most important component in the design of the rSEA, an analysis procedure based on Finite Element Method (FEM) is used in order to meet the requirements for the specific application. With a total weight of 2.53 kg, it is possible to directly mount the actuator on the frame of a knee orthosis. Torque and impedance controllers are implemented to ensure secure interaction with the patient and enable new strategies for rehabilitation. The design specifications as well as the controllers performance are verified by experiments.

Em virtude de uma elevada expectativa de vida mundial, faz-se crescente a probabilidade de ocorrer acidentes naturais e traumas físicos no cotidiano, o que ocasiona um aumento na demanda por reabilitação. A terapia física, sob o paradigma da reabilitação robótica com serious games, oferece maior motivação e engajamento do paciente ao tratamento, cujo emprego foi recomendado pela American Heart Association (AHA), apontando a mais alta avaliação (Level A) para pacientes internados e ambulatoriais. No entanto, o potencial de análise dos dados coletados pelos dispositivos robóticos envolvidos é pouco explorado, deixando de extrair informações que podem ser de grande valia para os tratamentos. O foco deste trabalho consiste na aplicação de técnicas para descoberta de conhecimento, classificando o desempenho de pacientes diagnosticados com hemiparesia crônica. Os pacientes foram inseridos em um ambiente de reabilitação robótica, fazendo uso do InMotion ARM, um dispositivo robótico para reabilitação de membros superiores e coleta dos dados de desempenho. Foi aplicado sobre os dados um roteiro para descoberta de conhecimento em bases de dados, desempenhando pré-processamento, transformação (extração de características) e então a mineração de dados a partir de algoritmos de aprendizado de máquina. A estratégia do presente trabalho culminou em uma classificação de padrões com a capacidade de distinguir lados hemiparéticos sob uma precisão de 94%, havendo oito atributos alimentando a entrada do mecanismo obtido. Interpretando esta coleção de atributos, foi observado que dados de força são mais significativos, os quais abrangem metade da composição de uma amostra. / As a result of a higher life expectancy, the high probability of natural accidents and traumas occurences entails an increasing need for rehabilitation. Physical therapy, under the robotic rehabilitation paradigm with serious games, offers the patient better motivation and engagement to the treatment, being a method recommended by American Heart Association (AHA), pointing the highest assessment (Level A) for inpatients and outpatients. However, the rich potential of the data analysis provided by robotic devices is poorly exploited, discarding the opportunity to aggregate valuable information to treatments. The aim of this work consists of applying knowledge discovery techniques by classifying the performance of patients diagnosed with chronic hemiparesis. The patients, inserted into a robotic rehabilitation environment, exercised with the InMotion ARM, a robotic device for upper-limb rehabilitation which also does the collection of performance data. A Knowledge Discovery roadmap was applied over collected data in order to preprocess, transform and perform data mining through machine learning methods. The strategy of this work culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having eight attributes feeding the input of the obtained mechanism. The interpretation of these attributes has shown that force-related data are more significant, comprising half of the composition of a sample.

Alors que de nombreux exosquelettes destinés à la rééducation neuromotrice ont été développés ces dernières années, ces dispositifs n'ont pas encore permis de vrai progrès dans la prise en charge des patients cérébrolésés. Une des clés pour améliorer les faibles résultats thérapeutiques obtenus serait de constamment adapter la thérapie robotisée en fonction de l'évolution du patient et de sa récupération, en adaptant l'assistance fournie par le robot pour maximiser l'engagement du patient. L'objectif de cette thèse est donc de comprendre les processus d'adaptations réciproques dans un contexte d'interaction physique Homme-Exosquelette. Dans un premier temps nous avons donc développé un nouveau type de contrôleur adaptatif qui assiste le sujet "au besoin", en modulant l'assistance fournie; et évalué différent signaux pour piloter cette adaptation afin de suivre au mieux la récupération du patient. Dans un deuxième temps, nous avons étudié l'adaptation de sujets sains à l'application de champs de forces distribués par un exosquelette sur leur bras durant la réalisation de mouvements dans l'espace. En effet, lors d'une interaction physique homme-robot, le sujet adapte aussi son comportement aux contraintes exercées par le robot. D'importantes différences inter-individuelles ont été observées, avec une adaptation à la contrainte imposée chez seulement 21% des sujets, mais avec des effets à-posteriori persistants mesurés chez 85% d'entre eux; ainsi qu'une généralisation dans l'espace de ces effets et un transfert à des contextes différents (hors du robot). Ces premiers résultats devraient permettre à terme d'améliorer la rééducation neuromotrice robotisée. / While many robotic exoskeletons have been developed for stroke rehabilitation in recent years, there were not yet improvements to the traditional therapy. A key to unleash the potentiality of robotics is to adapt the assistance provided by the robot in order to maximize the subject engagement and effort, by having the robotic therapy evolving with the patient recovery. For this reason, we aim at better understanding the process of reciprocal adaptation in a context of physical Human-Robot Interaction (pHRI). We first developed a new adaptive controller, which assists the subject "as-needed", by regulating its interaction to maximize the human involvement. We further compared different signals driving this adaptation, to better following the functional recovery level of the patients. While the control is performed by the robot, the subject is also adapting his movements, and this adaptation has not yet been studied when dealing with 3D movements and exoskeletons. Therefore, we exposed human motions to distributed force fields, generated by the exoskeleton at the joint level, to produce specific inter-joint coordination and to analyse the effects of this exposition. With healthy participants, we observed important inter-individual difference, with adaptation to the fields in 21% of the participants, but post-effects and persisting retention of these in time in 85% of the subjects, together with spatial generalization, and, preliminarily, transfer of the effects outside of the exoskeleton context. This work towards understanding pHRI could provide insights on innovative ways to develop new controllers for improving stroke motor recovery with exoskeletons.

Em virtude de uma elevada expectativa de vida mundial, faz-se crescente a probabilidade de ocorrer acidentes naturais e traumas físicos no cotidiano, o que ocasiona um aumento na demanda por reabilitação. A terapia física, sob o paradigma da reabilitação robótica com serious games, oferece maior motivação e engajamento do paciente ao tratamento, cujo emprego foi recomendado pela American Heart Association (AHA), apontando a mais alta avaliação (Level A) para pacientes internados e ambulatoriais. No entanto, o potencial de análise dos dados coletados pelos dispositivos robóticos envolvidos é pouco explorado, deixando de extrair informações que podem ser de grande valia para os tratamentos. O foco deste trabalho consiste na aplicação de técnicas para descoberta de conhecimento, classificando o desempenho de pacientes diagnosticados com hemiparesia crônica. Os pacientes foram inseridos em um ambiente de reabilitação robótica, fazendo uso do InMotion ARM, um dispositivo robótico para reabilitação de membros superiores e coleta dos dados de desempenho. Foi aplicado sobre os dados um roteiro para descoberta de conhecimento em bases de dados, desempenhando pré-processamento, transformação (extração de características) e então a mineração de dados a partir de algoritmos de aprendizado de máquina. A estratégia do presente trabalho culminou em uma classificação de padrões com a capacidade de distinguir lados hemiparéticos sob uma precisão de 94%, havendo oito atributos alimentando a entrada do mecanismo obtido. Interpretando esta coleção de atributos, foi observado que dados de força são mais significativos, os quais abrangem metade da composição de uma amostra. / As a result of a higher life expectancy, the high probability of natural accidents and traumas occurences entails an increasing need for rehabilitation. Physical therapy, under the robotic rehabilitation paradigm with serious games, offers the patient better motivation and engagement to the treatment, being a method recommended by American Heart Association (AHA), pointing the highest assessment (Level A) for inpatients and outpatients. However, the rich potential of the data analysis provided by robotic devices is poorly exploited, discarding the opportunity to aggregate valuable information to treatments. The aim of this work consists of applying knowledge discovery techniques by classifying the performance of patients diagnosed with chronic hemiparesis. The patients, inserted into a robotic rehabilitation environment, exercised with the InMotion ARM, a robotic device for upper-limb rehabilitation which also does the collection of performance data. A Knowledge Discovery roadmap was applied over collected data in order to preprocess, transform and perform data mining through machine learning methods. The strategy of this work culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having eight attributes feeding the input of the obtained mechanism. The interpretation of these attributes has shown that force-related data are more significant, comprising half of the composition of a sample.

Robôs de reabilitação como próteses ativas e exoesqueletos necessitam de atuadores capazes de atender certos requisitos como baixa impedância de saída, backdrivability, geração de torques grandes e precisos, e uma estrutura leve e compacta. Este trabalho apresenta o projeto de um Atuador Elástico em Série rotacional (AESr) para ser usado em uma prótese ativa para auxiliar na flexão/extensão da articulação do joelho durante a fisioterapia. O dispositivo é constituído de um motor de corrente contínua, um redutor de velocidade do tipo coroa e rosca sem-fim e uma mola torcional personalizada. Uma vez que o elemento elástico é o componente mais importante no projeto do AESr, um procedimento de análise baseado no Método dos Elementos Finitos (MEF) é utilizado para cumprir os requisitos definidos para a reabilitação do joelho. Com uma massa total de 2,53 Kg, é possível montar diretamente o atuador proposto em uma estrutura de prótese de joelho. Controladores de torque e impedância são implementados para assegurar uma interação segura com o paciente, permitindo que novas estratégias de reabilitação sejam avaliadas. As especificações do projeto bem como o desempenho dos controladores são validados experimentalmente. / Wearable robots, like prostheses, active orthosis and exoskeletons need of actuators able to meet certain requirements as low output impedance, backdrivability, precise and large torque generation, and a compact and lightweight design. This work presents the design of a rotary Series Elastic Actuator (rSEA) to be used in an active orthosis to assist in flexion/extension of the knee joint during physical therapy. The device includes a DC motor, a worm gear and a customized torsion spring. Since the elastic element is the most important component in the design of the rSEA, an analysis procedure based on Finite Element Method (FEM) is used in order to meet the requirements for the specific application. With a total weight of 2.53 kg, it is possible to directly mount the actuator on the frame of a knee orthosis. Torque and impedance controllers are implemented to ensure secure interaction with the patient and enable new strategies for rehabilitation. The design specifications as well as the controllers performance are verified by experiments.

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