Trunk Rehabilitation Using Cable-Driven Robotic Systems

Upper body control is required to complete many daily tasks. One needs to stabilize the head and trunk over the pelvis, as one shifts the center of mass to interact with the world. While healthy individuals can perform activities that require leaning, reaching, and grasping readily, those with neurological and musculoskeletal disorders present with control deficits. These deficits can lead to difficulty in shifting the body center of mass away from the stable midline, leading to functional limitations and a decline in the quality of activity. Often these patient groups use canes, walkers, and wheelchairs for support, leading to occasional strapping or joint locking of the body for trunk stabilization. Current rehabilitation strategies focus on isolated components of stability. This includes strengthening, isometric exercises, hand-eye coordination tasks, isolated movement, and proprioceptive training. Although all these components are evidence based and directly correlate to better stability, motor learning theories such as those by Nikolai Bernstein, suggest that task and context specific training can lead to better outcomes. In specific, based on our experimentation, we believe functional postural exploration, while encompassing aspects of strengthening, hand-eye coordination, and proprioceptive feedback can provide better results. In this work, we present two novel cable robotic platforms for seated and standing posture training. The Trunk Support Trainer (TruST) is a platform for seated posture rehabilitation that provides controlled external wrench on the human trunk in any direction in real-time. The Stand Trainer is a platform for standing posture rehabilitation that can control the trunk, pelvis, and knees, simultaneously. The system works through the use of novel force-field algorithms that are modular and user-specific. The control uses an assist-as-needed strategy to apply forces on the user during regions of postural instability. The device also allows perturbations for postural reactive training. We have conducted several studies using healthy adult populations and pilot studies on patient groups including cerebral palsy, cerebellar ataxia, and spinal cord injury. We propose new training methods that incorporate motor learning theory and objective interventions for improving posture control. We identify novel methods to characterize posture in form of the “8-point star test”. This is to assess the postural workspace. We also demonstrate novel methods for functional training of posture and balance. Our results show that training with our robotic platforms can change the trunk kinematics. Specifically, healthy adults are able to translate the trunk further and rotate the trunk more anteriorly in the seated position. In the standing position, they can alter their reach strategy to maintain the upper trunk more vertically while reaching. Similarly, Cerebral Palsy patients improve their trunk translations, reaching workspace, and maintain a more vertical posture after training, in the seated position. Our results also showed that an Ataxia patient was able to improve their reaching workspace and trunk translations in the standing position. Finally, our results show that the robotic platforms can successfully reduce trunk and pelvis sway in spinal cord injury patients. The results of the pilot studies suggest that training with our robotic platforms and methods is beneficial in improving trunk control

Design and Development of Assistive Robots for Close Interaction with People with Disabilities

People with mobility and manipulation impairments wish to live and perform tasks as independently as possible; however, for many tasks, compensatory technology does not exist, to do so. Assistive robots have the potential to address this need. This work describes various aspects of the development of three novel assistive robots: the Personal Mobility and Manipulation Appliance (PerMMA), the Robotic Assisted Transfer Device (RATD), and the Mobility Enhancement Robotic Wheelchair (MEBot). PerMMA integrates mobility with advanced bi-manual manipulation to assist people with both upper and lower extremity impairments. The RATD is a wheelchair mounted robotic arm that can lift higher payloads and its primary aim is to assist caregivers of people who cannot independently transfer from their electric powered wheelchair to other surfaces such as a shower bench or toilet. MEBot is a wheeled robot that has highly reconfigurable kinematics, which allow it to negotiate challenging terrain, such as steep ramps, gravel, or stairs. A risk analysis was performed on all three robots which included a Fault Tree Analysis (FTA) and a Failure Mode Effect Analysis (FMEA) to identify potential risks and inform strategies to mitigate them. Identified risks or PerMMA include dropping sharp or hot objects. Critical risks identified for RATD included tip over, crush hazard, and getting stranded mid-transfer, and risks for MEBot include getting stranded on obstacles and tip over. Lastly, several critical factors, such as early involvement of people with disabilities, to guide future assistive robot design are presented

A novel robotic platform to assist, train, and study head-neck movement

Moving the head-neck freely is an everyday task that a healthy person takes for granted. Such a simple movement, however, may be very challenging for individuals with neurological disorders such as amyotrophic lateral sclerosis. These individuals often do not have enough neck muscle strength to stabilize the head at the upright neutral or to move it in a controlled manner. Static braces are commonly prescribed to these patients. However, these braces often fix the head at a single configuration, which makes them uncomfortable to wear for an extended period of time. In this thesis, a robotic neck brace is developed. It accommodates three rotations and covers roughly 70% range of motion of the head-neck of a typical able-bodied adult. The hardware is lightweight (1.5 kilogram) and wearable, with a pair of pads and a soft band attached to the shoulders and the forehead, respectively. A parallel mechanism connecting the shoulder pads and the headband was designed to meet the empirical human movement data. This design choice is novel where the parasitic motion (translation of the head) was parameterized and optimized to address misalignment between the robot and the user's head. A user can control this neck brace to assist intended head-neck movement through input devices, including hand-held joysticks, keyboards, and eye-trackers. This provides a potential solution to remediate head drop. Additionally, this robotic brace is developed into a versatile platform to train and study head-neck movements. The robot was designed to be highly transparent to the user and features different force controllers. Therefore, it can be used to assess the free movement of the head-neck and mimic different interactions between a therapist and a patient. The modalities of this neck brace have been validated with different users. To the best of our knowledge, this robotic neck brace is the first in the literature to assist, train, and study head-neck movements

Instrumentation and validation of a robotic cane for transportation and fall prevention in patients with affected mobility

Dissertação de mestrado integrado em Engenharia Física, (especialização em Dispositivos, Microssistemas e Nanotecnologias)O ato de andar é conhecido por ser a forma primitiva de locomoção do ser humano, sendo que este traz muitos benefícios que motivam um estilo de vida saudável e ativo. No entanto, há condições de saúde que dificultam a realização da marcha, o que por consequência pode resultar num agravamento da saúde, e adicionalmente, levar a um maior risco de quedas. Nesse sentido, o desenvolvimento de um sistema de deteção e prevenção de quedas, integrado num dispositivo auxiliar de marcha, seria essencial para reduzir estes eventos de quedas e melhorar a qualidade de vida das pessoas. Para ultrapassar estas necessidades e limitações, esta dissertação tem como objetivo validar e instrumentar uma bengala robótica, denominada Anti-fall Robotic Cane (ARCane), concebida para incorporar um sistema de deteção de quedas e um mecanismo de atuação que possibilite a prevenção de quedas, ao mesmo tempo que assiste a marcha. Para esse fim, foi realizada uma revisão do estado da arte em bengalas robóticas para adquirir um conhecimento amplo e aprofundado dos componentes, mecanismos e estratégias utilizadas, bem como os protocolos experimentais, principais resultados, limitações e desafios em dispositivos existentes. Numa primeira fase, foi estipulado o objetivo de: (i) adaptar a missão do produto; (ii) estudar as necessidades do consumidor; e (iii) atualizar as especificações alvo da ARCane, continuação do trabalho de equipa, para obter um produto com design e engenharia compatível com o mercado. Foi depois estabelecida a arquitetura de hardware e discutidos os componentes a ser instrumentados na ARCane. Em seguida foram realizados testes de interoperabilidade a fim de validar o funcionamento singular e coletivo dos componentes. Relativamente ao controlo de movimento, foi desenvolvido um sistema inovador, de baixo custo e intuitivo, capaz de detetar a intenção do movimento e de reconhecer as fases da marcha do utilizador. Esta implementação foi validada com seis voluntários saudáveis que realizaram testes de marcha com a ARCane para testar sua operabilidade num ambiente de contexto real. Obteve-se uma precisão de 97% e de 90% em relação à deteção da intenção de movimento e ao reconhecimento da fase da marcha do utilizador. Por fim, foi projetado um método de deteção de quedas e mecanismo de prevenção de quedas para futura implementação na ARCane. Foi ainda proposta uma melhoria do método de deteção de quedas, de modo a superar as limitações associadas, bem como a proposta de dispositivos de deteção a serem implementados na ARCane para obter um sistema completo de deteção de quedas.The act of walking is known to be the primitive form of the human being, and it brings many benefits that motivate a healthy and active lifestyle. However, there are health conditions that make walking difficult, which, consequently, can result in worse health and, in addition, lead to a greater risk of falls. Thus, the development of a fall detection and prevention system integrated with a walking aid would be essential to reduce these fall events and improve people quality of life. To overcome these needs and limitations, this dissertation aims to validate and instrument a cane-type robot, called Anti-fall Robotic Cane (ARCane), designed to incorporate a fall detection system and an actuation mechanism that allow the prevention of falls, while assisting the gait. Therefore, a State-of-the-Art review concerning robotic canes was carried out to acquire a broad and in-depth knowledge of the used components, mechanisms and strategies, as well as the experimental protocols, main results, limitations and challenges on existing devices. On a first stage, it was set an objective to (i) enhance the product's mission statement; (ii) study the consumer needs; and (iii) update the target specifications of the ARCane, extending teamwork, to obtain a product with a market-compatible design and engineering that meets the needs and desires of the ARCane users. It was then established the hardware architecture of the ARCane and discussed the electronic components that will instrument the control, sensory, actuator and power units, being afterwards subjected to interoperability tests to validate the singular and collective functioning of cane components altogether. Regarding the motion control of robotic canes, an innovative, cost-effective and intuitive motion control system was developed, providing user movement intention recognition, and identification of the user's gait phases. This implementation was validated with six healthy volunteers who carried out gait trials with the ARCane, in order to test its operability in a real context environment. An accuracy of 97% was achieved for user motion intention recognition and 90% for user gait phase recognition, using the proposed motion control system. Finally, it was idealized a fall detection method and fall prevention mechanism for a future implementation in the ARCane, based on methods applied to robotic canes in the literature. It was also proposed an improvement of the fall detection method in order to overcome its associated limitations, as well as detection devices to be implemented into the ARCane to achieve a complete fall detection system

Limited Information Shared Control and its Applications to Large Vehicle Manipulators

Diese Dissertation beschäftigt sich mit der kooperativen Regelung einer mobilen Arbeitsmaschine, welche aus einem Nutzfahrzeug und einem oder mehreren hydraulischen Manipulatoren besteht. Solche Maschinen werden für Aufgaben in der Straßenunterhaltungsaufgaben eingesetzt. Die Arbeitsumgebung des Manipulators ist unstrukturiert, was die Bestimmung einer Referenztrajektorie erschwert oder unmöglich macht. Deshalb wird in dieser Arbeit ein Ansatz vorgeschlagen, welcher nur das Fahrzeug automatisiert, während der menschliche Bediener ein Teil des Systems bleibt und den Manipulator steuert. Eine solche Teilautomatisierung des Gesamtsystems führt zu einer speziellen Klasse von Mensch-Maschine-Interaktionen, welche in der Literatur noch nicht untersucht wurde: Eine kooperative Regelung zwischen zwei Teilsystemen, bei der die Automatisierung keine Informationen von dem vom Menschen gesteuerten Teilsystem hat. Deswegen wird in dieser Arbeit ein systematischer Ansatz der kooperativen Regelung mit begrenzter Information vorgestellt, der den menschlichen Bediener unterstützen kann, ohne die Referenzen oder die Systemzustände des Manipulators zu messen. Außerdem wird ein systematisches Entwurfskonzept für die kooperative Regelung mit begrenzter Information vorgestellt. Für diese Entwurfsmethode werden zwei neue Unterklassen der sogenannten Potenzialspiele eingeführt, die eine systematische Berechnung der Parameter der entwickelten kooperativen Regelung ohne manuelle Abstimmung ermöglichen. Schließlich wird das entwickelte Konzept der kooperativen Regelung am Beispiel einer großen mobilen Arbeitsmaschine angewandt, um seine Vorteile zu ermitteln und zu bewerten. Nach der Analyse in Simulationen wird die praktische Anwendbarkeit der Methode in drei Experimenten mit menschlichen Probanden an einem Simulator untersucht. Die Ergebnisse zeigen die Überlegenheit des entwickelten kooperativen Regelungskonzepts gegenüber der manuellen Steuerung und der nicht-kooperativen Steuerung hinsichtlich sowohl der objektiven Performanz als auch der subjektiven Bewertung der Probanden. Somit zeigt diese Dissertation, dass die kooperative Regelung mobiler Arbeitsmaschinen mit den entwickelten theoretischen Konzepten sowohl hilfreich als auch praktisch anwendbar ist

A deep learning solution for real-time human motion decoding in smart walkers

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)The treatment of gait impairments has increasingly relied on rehabilitation therapies which benefit from the use of smart walkers. These walkers still lack advanced and seamless Human-Robot Interaction, which intuitively understands the intentions of human motion, empowering the user’s recovery state and autonomy, while reducing the physician’s effort. This dissertation proposes the development of a deep learning solution to tackle the human motion decoding problematic in smart walkers, using only lower body vision information from a camera stream, mounted on the WALKit Smart Walker, a smart walker prototype for rehabilitation purposes. Different deep learning frameworks were designed for early human motion recognition and detec tion. A custom acquisition method, including a smart walker’s automatic driving algorithm and labelling procedure, was also designed to enable further training and evaluation of the proposed frameworks. Facing a 4-class (stop, walk, turn right/left) classification problem, a deep learning convolutional model with an attention mechanism achieved the best results: an offline f1-score of 99.61%, an online calibrated instantaneous precision higher than 97% and a human-centred focus slightly higher than 30%. Promising results were attained for early human motion detection, with enhancements in the focus of the proposed architectures. However, further improvements are still needed to achieve a more reliable solution for integration in a smart walker’s control strategy, based in the human motion intentions.O tratamento de distúrbios da marcha tem apostado cada vez mais em terapias de reabilitação que beneficiam do uso de andarilhos inteligentes. Estes ainda carecem de uma Interação Humano-Robô avançada e eficaz, capaz de entender, intuitivamente, as intenções do movimento humano, fortalecendo a recuperação autónoma do paciente e reduzindo o esforço médico. Esta dissertação propõe o desenvolvimento de uma solução de aprendizagem para o problema de descodificação de movimento humano em andarilhos inteligentes, usando apenas vídeos recolhidos pelo WALKit Smart Walker, um protótipo de andarilho inteligente usado para reabilitação. Foram desenvolvidos algoritmos de aprendizagem para o reconhecimento e detecção precoces de movimento humano. Um método de aquisição personalizado, incluindo um algoritmo de condução e labelização automatizados, foi projetado para permitir o conseguinte treino e avaliação dos algoritmos propostos. Perante a classificação de 4 ações (parar, andar, virar à direita/esquerda), um modelo convolucional com um mecanismo de atenção alcançou os melhores resultados: f1-score offline de 99,61%, precisão instantânea calibrada online de superior a 97 % e um foco centrado no ser humano ligeiramente superior a 30%. Com esta dissertação alcançaram-se resultados promissores para a detecção precoce de movimento humano, com aprimoramentos no foco dos algoritmos propostos. No entanto, ainda são necessárias melhorias adicionais para alcançar uma solução mais robusta para a integração na estratégia de controlo de um andarilho inteligente, com base nas intenções de movimento do utilizador