Musculoskeletal Modeling and Control of the Human Upper Limb during Manual Wheelchair Propulsion: Application in Functional Electrical Stimulation Rehabilitation Therapy

Manual wheelchair users rely on their upper limbs for independence and daily activities. The high incidence of upper limb injuries can be attributed to the significant muscular demands imposed by propulsion as a repetitive movement. People with spinal cord injury are at high risk for upper limb injuries, including neuromusculoskeletal pathologies and nociceptive pain, as human upper limbs are poorly designed to facilitate chronic weight-bearing activities, such as manual wheelchair propulsion. Comprehending the underlying biomechanical mechanisms of motor control and developing appropriate rehabilitation tasks are essential to deal with the effects of poor motor control on the performance of manual wheelchair users and prevent long-term upper limb disability, which can interrupt electrical signals between the brain and muscles. Functional electrical stimulation utilizes low-intensity electrical signals to artificially generate body movements by stimulating the damaged peripheral nerves of patients with impaired motor control. Therefore, this study investigates the central nervous system strategy to control human movements, which can be used for task-specific functional electrical stimulation rehabilitation therapy. To this aim, two degrees of freedom musculoskeletal model of the upper limb, including six muscles, is developed, and an optimal controller consisting of two separate optimal parts is proposed to track the desired trajectories in the joint space and estimate the optimal muscle activations regarding physiological constraints. The simulation results are validated with electromyography datasets collected from twelve participants. This study's primary advantages are generating optimal joint torques, accurate trajectory tracking, and good similarities between estimated and measured muscle activations

Neuro-fuzzy modeling of multi-field surface neuroprostheses for hand grasp

154 p.Las neuroprótesis aplican pulsos eléctricos a los nervios periféricos con el objetivo de sustituir funciones motrices/sensoriales perdidas, dando asistencia e influyendo positivamente en la rehabilitación motriz de personas con disfunciones motrices causadas por trastornos neurológicos. La complejidad de la neuroanatomía del antebrazo y la mano, su dimensionalidad, las diversas tareas no-cíclicas, la variabilidad de movimientos entre sujetos y la reducida selectividad de las neuroprótesis superficiales, ha dado lugar al diseño de un número reducido de neuroprótesis orientadas a agarres básicos. La posibilidad de hacer más selectiva la estimulación mediante los electrodos multi-campo, junto con el conocimiento sobre la incomodidad y los movimientos que genera la aplicación de la estimulación eléctrica funcional (FES por sus siglas en inglés) en miembro superior, podrían ser base fundamental para el desarrollo de neuroprótesis de agarre más avanzadas. La presente tesis describe un análisis de incomodidad como resultado de FES en el miembro superior, y propone modelos neuro-difusos para neuroprótesis de agarre tanto para personas sanas como para personas con trastornos neurológicos. El conocimiento generado respecto a la incomodidad puede ser utilizado como guía para desarrollar aplicaciones de FES de miembro superior más cómodas. Del mismo modo, los modelos propuestos en esta tesis pueden ser utilizados para apoyar el diseño y la validación de sistemas de control avanzados en neuroprótesis dirigidas a la función de agarre.Tecnalia; Intelligent Control Research Grou

De animais a máquinas : humanos tecnicamente melhores nos imaginários de futuro da convergência tecnológica

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Sociais, Departamento de Sociologia, 2020.O tema desta investigação é discutir os imaginários sociais de ciência e tecnologia que emergem a partir da área da neuroengenharia, em sua relação com a Convergência Tecnológica de quatro disciplinas: Nanotecnologia, Biotecnologia, tecnologias da Informação e tecnologias Cognitivas - neurociências- (CT-NBIC). Estas áreas desenvolvem-se e são articuladas por meio de discursos que ressaltam o aprimoramento das capacidades físicas e cognitivas dos seres humanos, com o intuito de construir uma sociedade melhor por meio do progresso científico e tecnológico, nos limites das agendas de pesquisa e desenvolvimento (P&D). Objetivos: Os objetivos nesse cenário, são discutir as implicações éticas, econômicas, políticas e sociais deste modelo de sistema sociotécnico. Nos referimos, tanto as aplicações tecnológicas, quanto as consequências das mesmas na formação dos imaginários sociais, que tipo de relações se estabelecem e como são criadas dentro desse contexto. Conclusão: Concluímos na busca por refletir criticamente sobre as propostas de aprimoramento humano mediado pela tecnologia, que surgem enquanto parte da agenda da Convergência Tecnológica NBIC. No entanto, as propostas de melhoramento humano vão muito além de uma agenda de investigação. Há todo um quadro de referências filosóficas e políticas que defendem o aprimoramento da espécie, vertentes estas que se aliam a movimentos trans-humanistas e pós- humanistas, posições que são ao mesmo tempo éticas, políticas e econômicas. A partir de nossa análise, entendemos que ciência, tecnologia e política estão articuladas, em coprodução, em relação às expectativas de futuros que são esperados ou desejados. Ainda assim, acreditamos que há um espaço de diálogo possível, a partir do qual buscamos abrir propostas para o debate público sobre questões de ciência e tecnologia relacionadas ao aprimoramento da espécie humana.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The subject of this research is to discuss the social imaginaries of science and technology that emerge from the area of neuroengineering in relation with the Technological Convergence of four disciplines: Nanotechnology, Biotechnology, Information technologies and Cognitive technologies -neurosciences- (CT-NBIC). These areas are developed and articulated through discourses that emphasize the enhancement of human physical and cognitive capacities, the intuition it is to build a better society, through the scientific and technological progress, at the limits of the research and development (R&D) agendas. Objectives: The objective in this scenery, is to discuss the ethic, economic, politic and social implications of this model of sociotechnical system. We refer about the technological applications and the consequences of them in the formation of social imaginaries as well as the kind of social relations that are created and established in this context. Conclusion: We conclude looking for critical reflections about the proposals of human enhancement mediated by the technology. That appear as a part of the NBIC technologies agenda. Even so, the proposals of human enhancement go beyond boundaries that an investigation agenda. There is a frame of philosophical and political references that defend the enhancement of the human beings. These currents that ally to the transhumanism and posthumanism movements, positions that are ethic, politic and economic at the same time. From our analysis, we understand that science, technology and politics are articulated, are in co-production, regarding the expected and desired futures. Even so, we believe that there is a space of possible dialog, from which we look to open proposals for the public discussion on questions of science and technology related to enhancement of human beings

Evaluation of transfemoral prosthesis performance control using artificial neural network controllers

Transfemoral (Above-knee) amputation of the leg of an individual as a result of traumatic injury or due to complications arising out of diabetes or vascular disorders is a common occurrence worldwide. Following the surgical amputation procedure, the subject is fitted with prosthetic leg to help regain mobility. Prosthetic sockets are designed to transfer the body weight to the leg during locomotion. During normal human gait, the lower limbs perform four major functions: balance, positioning, support, and power. Prosthetic legs currently available in the market are mostly passive devices that provide limited support and functionality during walking. These devices also have limited adaptability during walking or to enable a more active lifestyle. The common problems of the existing above-knee prosthesis for the unilateral amputees include asymmetry between motion of the prosthetic leg with the intact leg, reduced speed along with increased energy expenditure. Not only that, but there are also different types of forces, counter forces and errors associated with gait which was ignored in some active prosthesis designs. If these technical problems are left un-addressed, they may end up with secondary medical issues requiring further surgery. While it is desirable for the prosthetic limb to have similar or close efficiency or tracking to the intact limb, it is more important for the prosthetic leg to be able to replicate the movement of a normal human leg as much as possible. Most of the studies earlier were limited to pathological gait tests in laboratory environments using inertial sensor/motion trackers which restricted the mobility of the individuals. Recently, smarter data acquisition systems are designed to capture the human locomotion in an easier and effective way. Combination of these factors result in greater advancement of prosthetic research. Prior research in lower-limb amputee gait has focused mostly trans-tibial (below knee) amputees as they are the highest in number. In general, available prostheses for people with lower limb amputation are primarily passive devices whose performance cannot be adjusted or optimized to meet the requirements of different users. The adverse complications of wearing poorly functioning prosthetic devices include asymmetric gait, increased metabolic energy consumption, limited blood flow, instability, sores, and joint pain. The amputees might have to undergo further joint (knee/hip) replacement procedure and that increases the chance of the increased number of trans femoral amputee in the long run. There exists a high and increasing demand for an advanced prosthetic foot that is comfortable and able to replicate the function of the biological foot. Trans-femoral amputees are the second highest and the research is more challenging as the amputees lost two of their vital joints (ankle and knee). So, to design an efficient prosthetic ankle-knee system, (including all the challenges for transtibial amputees) it is very important to consider the coupling effects of the two joints and different associated errors, or force associated with the gait like ground reaction force. Currently available prosthetic knees are either simple mechanical hinges or sophisticated computer controlled. Development of active powered prosthetic knees (focused on the control with little emphasis) results in uncomfortable, low efficient, low energy consuming device. The inherent nonlinearities of the actuators make it difficult to control. Again, interaction forces between residual limb and the socket are dynamic in nature and are a result of gait pattern of individuals, interaction of the feet with the terrain, and the transfer of rest of the body weight during gait. These factors made the prosthetic device control and design advancement challenging for researchers. Earlier literatures address assessing gait symmetry, movement of the healthy joints, activities of the residual muscles and the metabolic energy consumption in individuals who had undergone traditional amputation. There were research studies done showing considerable residual muscle activity in the transtibial and transfemoral amputees and minimal or random muscle activity based on the co-relation between residuum socket interface (RSI) force and EMG to the type of gait. These forces are a source of interest for researchers to investigate for better controlling. Adaptive controllers like PD, PID and combinations are used in the development of active prosthetic devices. But PID and other traditional adaptive controllers cannot handle these nonlinearities and challenges of human locomotion properly. Moreover, most of the designs do not have consistent performance over the total gait cycle or consecutive steps. All prostheses require some sort of stability mechanism, either manual or a weight-activated locking system. The main joints made of mechanical hinges should control the flexion and extension motion to mimic human gait. For unilateral amputee, the development of Artificial optimized neural network controller is important in this regard as it can train the neurons with the input data from the intact leg and mimic similar trajectory for the residual limb to follow. This dissertation addresses the limitations of traditional controllers in an orderly fashion by building a strong platform to develop intelligent knee-ankle prosthesis system. The following are the key steps adopted in this dissertation. • First, a mathematical model will be developed for a leg movement during normal gait. Algorithms for gait analysis will be developed to study the gait of people with above-knee amputation in real time during work-related activities. Simulations will be done to observe the performance of the controller. • A more reliable and realistic learning-based control strategy will be developed to adaptively compensate for the unknown, changing ankle-knee dynamics and drive the prosthetic ankle-knee joint along the desired trajectories. Different combinations of control parameters will be changed to see the performance improvement and error reduction. Comparative results will be shown for different controllers. • Finally, a framework for experimental transfemoral amputee gait study will be proposed to collect data using force sensors and EMG sensors attached to the residual limbs and muscles during work related activities and normal gait. It is anticipated that the learning capabilities of the control strategies will enable the prosthetic ankle-knee joints to not only replicate the movement of the healthy knee-ankle system, but also improve the stability of the gait and optimize the performance to a great extent. Learning-based control of the prosthetic ankle-knee joint algorithms used here consider the ankle-knee dynamics, foot-ground interaction, and the movement of the rest of the body to make it appropriate to be used for transfemoral unilateral amputee. The first strategy uses an artificial neural network-based controller to learn the unknown and changing dynamics of the ankle-knee joint and to track a desired ankle knee displacement profile. In the subsequent strategies, the neural dynamic programming-based controller is improvised by increasing the number of neurons and other parameters, comparative performance was shown for two joints also. Later a centralized controller is used to control both the joints. Additional PID is used and comparative analysis between controller schemes are presented to have a balanced and better control. Actual gait data (obtained from the healthy human subjects) of this dissertation is used to study the effectiveness of the controller. It will be interesting to see the performance of the adaptive neural network controller for unilateral transfemoral amputee with changes in terrain and in user requirements. It is anticipated that the strategy developed in this dissertation will help build an intelligent prosthetic system that can significantly improve the mobility and long-term health of people with lower limb amputation followed by proper rehabilitation

Combining reinforcement learning and optimal control for the control of nonlinear dynamical systems

This thesis presents a novel hierarchical learning framework, Reinforcement Learning Optimal Control, for controlling nonlinear dynamical systems with continuous states and actions. The adapted approach mimics the neural computations that allow our brain to bridge across the divide between symbolic action-selection and low-level actuation control by operating at two levels of abstraction. First, current findings demonstrate that at the level of limb coordination human behaviour is explained by linear optimal feedback control theory, where cost functions match energy and timing constraints of tasks. Second, humans learn cognitive tasks involving learning symbolic level action selection, in terms of both model-free and model-based reinforcement learning algorithms. We postulate that the ease with which humans learn complex nonlinear tasks arises from combining these two levels of abstraction. The Reinforcement Learning Optimal Control framework learns the local task dynamics from naive experience using an expectation maximization algorithm for estimation of linear dynamical systems and forms locally optimal Linear Quadratic Regulators, producing continuous low-level control. A high-level reinforcement learning agent uses these available controllers as actions and learns how to combine them in state space, while maximizing a long term reward. The optimal control costs form training signals for high-level symbolic learner. The algorithm demonstrates that a small number of locally optimal linear controllers can be combined in a smart way to solve global nonlinear control problems and forms a proof-of-principle to how the brain may bridge the divide between low-level continuous control and high-level symbolic action selection. It competes in terms of computational cost and solution quality with state-of-the-art control, which is illustrated with solutions to benchmark problems.Open Acces

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp