Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion.

The biomechanics and muscle activity of forward and backward slope walking was investigated in humans to gain additional insight into neural control strategies. An adjustable instrumented ramped walkway was constructed and validated. Kinematic, ground reaction force, and muscle activity data were collected from nine subjects walking at three grades (0%, 15%, and 39%) for each of four conditions (forward upslope and downslope and backward upslope and downslope). The changes observed in the data were generally progressive from 0% to 15% to 39% grade. During forward downslope walking the joint moment pattern at the knee changed significantly, power absorption increased, and changes in the muscle activity patterns corresponded directly to changes in joint mechanics. During forward upslope walking, the hip joint moment pattern changed significantly, power generation increased, and changes in the muscle activity pattern were not directly related to changes in the joint moments at all joints. The muscle activity pattern data suggest that modifications to the level walking control strategies were necessary during slope walking. Backward slope walking was used to further explore these findings. Backward upslope and forward downslope kinematics and kinetics were similar, as were those from backward downslope and forward upslope walking. However, power generation increased during upslope walking tasks and power absorption increased during downslope walking tasks, and the changes in muscle firing patterns were more similar for these tasks than for those with similar kinetics. Increased power generation required compensatory muscle activity at adjacent joints that was not directly related to the moments at those joints; increased power absorption did not require such compensatory activity, and muscle activity was directly related to the joint moments. Overall, these data suggest that changes in the control strategy and/or modifications of the level walking control strategy are strongly influenced by the power demands of a task. The characterization of forward and backward slope walking presented here is novel and has important implications for many patient populations; knowledge of the task mechanics may be used to develop or improve physical therapy and rehabilitation exercise programs as well as the design of replacement and/or assistive devices.

Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion.

No description available.

Falls from heights (FFH) are one of the leading causes of fatalities in skilled labor divisions such as construction, mining, agriculture/forestry, and manufacturing. Previous research has established that localized muscle fatigue (LMF) increases center of mass (COM)- and center of pressure (COP)-based measures of quiet stance. This is important because these increases have been linked to elevated risk of falls, and workers in the construction industry frequently engage in fatiguing activities while working at heights. In addition, the rate of fatality due to an occupational fall increases exponentially with age. Improved methods of fall prevention may be obtained through increased understanding of factors that have a deleterious effect on balance and postural control such as LMF and aging. An initial study was conducted to investigate the effects of LMF and aging on balance recovery from a postural perturbation without stepping. Sagittal plane postural perturbations were administered to young and older groups of participants before and after exercises to fatigue the lumbar extensors or ankle plantar flexors. Measures of balance recovery were based on the COM and COP trajectories and the maximum perturbation that could be withstood without stepping. Balance recovery measures were consistent with an LMF-induced decrement to recover from perturbations without stepping. Aging was also associated with an impaired ability to recover from the perturbations. The second study in the series investigated the effects of aging and LMF on the neural control of upright stance during small postural perturbations. Small magnitude postural perturbations were administered to young and older groups before and after fatiguing exercises. A single degree of freedom (DOF) model of the human body was developed that accurately simulated the experimentally collected kinematics during recovery from the perturbations. The model was controlled by invariant feedback gains that operated on the time-delayed kinematics. Feedback gains and time-delay were optimized for each participant, and a novel delay margin analysis was performed to assess system robustness toward instability. Results indicated that older individuals had a longer "effective" time-delay and exhibited greater reliance on afferent velocity information. No changes in feedback controller gains, time-delay, or delay margins were found with LMF in either age group. The final study investigated the use of a nonlinear controller to simulate responses to large magnitude postural perturbations. A three DOF model of the human body was developed and controlled with the state-dependent Riccati equation (SDRE). Parameters of the SDRE were optimized to fit the experimentally recorded kinematics. Unlike other forms of nonlinear control, the SDRE provides meaningful parameters for interpretation in the system identification. The SDRE approach was successful at stabilizing the dynamical system; however, accurate results were not obtained. Reasons for these errors are discussed, and an alternative formulation to the time-delayed optimal control problem using Roesser state space equations is presented. / Ph. D.

Rigidity is one of the cardinal symptoms in Parkinson's disease, along with Bradykinesia, tremor and postural instability. Rigidity in PD has been understudied, but its pathophysiological basis remains unclear. Various types of neurophysiological and biomechanical approach have been developed in order to investigate the neural control of muscle tone. A common approach is to observe the sensitivity of muscle resistance in response to stretch velocity or displacement [Kamper, Rea, He]. A recent study on elbow flexors in patients with spasticity and rigidity showed a velocity dependent increase in reactive torque in both groups [Lee H, et al). Even though this Study shows a correlation between elbow flexors and velocity, it doesn't discuss the role of elbow extensors. We studied the rigidity response in the elbow of both arms to different speed movements in 12 patients suffering from Parkinson's disease ON or OFF medication. The purpose of this study was to look at both elbow flexion and extension and show that quantitative measures of rigidity and movement disorders in subjects with Parkinson's disease correlate with the currently used clinical evaluations and also find the correlation between velocity and both elbow extension and flexion at the same time. Elbow was flexed and extended by means of a robotic arm,under four different speeds. The resistance to movement was recorded with a torque sensor and EMG of two elbow muscles; Biceps and Triceps; was recorded while the subjects were attempting to relax. The patients were also examined by physicians and their elbow rigidity and muscle tone and Parkinson's disease stage was evaluated and a Universal score in the categories of UPDRS, MMSE, and CAPIT was assigned for each arm of each individual. In the end we will argue that there is a very strong correlation between speed and elbow Extension and Flexion, muscle activity and the rigidity presented in each arm. We will also present the correlation between the robotic torque measurement and the clinical scores given to each subject.

The main goal of this paper is to test the applicability of the device for neural computer control on a group of ten volunteers. In the next part of the paper author focuses on Electroencephalography and the conversion of analog neural signals from brain to digital form. Next chapter describes currently on the market available devices, which allow customers direct computer controlling with the usage of bio signal from brain. The device selected for the purposes of this paper (Emotiv Epoc) is more described in detail. The last goal is an attempt to predict the future development of this technology. The paper demonstrates applicability of this device in its current form for everyday work with Microsoft Project and presents users who are able to learn to control a computer with this device in just 980 minutes of training.

Introdução: O quadro hipertensivo pode ser agravado quando associado ao envelhecimento, e a prática do exercício físico está relacionada a uma melhora do quadro hipertensivo. Métodos: Foram utilizados ratos SHR e WKY com 12 meses de idade, divididos em dois grupos: treinados (T) e sedentários (S). Resultados: O desempenho na esteira dos animais WKY e SHR treinados foi maior na quarta na oitava semana. Não houve diferença no peso corporal dos animais. A PAS, PAD e PAM dos animais SHR treinados por 2 e 8 semanas foi menor do que a encontrada nos animais sedentários de 8 semanas . O componente HF foi maior nos animais treinados por 8 semanas, em comparação aos grupos sedentários . O exercício físico não foi capaz de modificar a contagem de neurônios positivos para ChAT e TH, tanto nos animais SHR quanto nos animais WKY. Conclusão: O exercício físico foi capaz de melhorar o desempenho em esteira, diminuir a pressão arterial sistólica, diastólica e média, melhorar a sensibilidade barorreflexa e aumentar o componente HF para o coração. / Introduction: The hypertension can be aggravated when associated with aging, and the practice of physical exercise is related to an improvement of the hypertensive picture. Methods: Twelve-month-old SHR and WKY rats were divided into two groups: trained (T) and sedentary (S). Results: The performance of WKY and SHR trained animals was higher in the fourth and eighth week. There was no difference in the body weight of the animals. The SBP, DBP and MAP of SHR animals trained for 2 and 8 weeks was lower than that found in sedentary animals of 8 weeks. The HF component was higher in trained animals for 8 weeks compared to sedentary groups. Physical exercise was not able to modify the number of ChAT and TH positive neurons in both SHR and WKY animals. Conclusion: Physical exercise was able to improve treadmill performance, decrease systolic, diastolic and mean blood pressure, improve baroreflex sensitivity and increase the HF component to the heart.

Limb loss has severe physical and psychological effects on individuals with upper limb amputations. Higher rates of prosthetic device abandonment has contributed to a need for prosthetic hands that are functional and comfortable for the user. Prosthetic hands have been abandoned for many reasons including weight, size, limited functionality, training time, and discomfort. An optimal prosthetic hand considers both neural control and sensory feedback. Neural control of the prosthetic is crucial to obtain accuracy and desirable functions. Popular methods of sensory feedback such as visual feedback are mentally exhausting and require constant focus from the user. Control and feedback of prosthetic devices differs based on the type of prosthetic. Passive, myoelectric, body-powered, electrocorticographic, adaptive, and sonomyographic prosthetic hand devices focus on a variety of hand movements and each utilizes different methods of control. It is also important to consider the biomaterials of prosthetic hands to enhance comfort and ease-of-use. Mechanical and AM-ULA testing ensure prosthetic hands can perform necessary movements for the user. To develop an ideal prosthetic hand, control and feedback must be considered along with comfort and functionality of the device.

Este trabalho apresenta técnicas de projeto de controle neural e controle difuso para uma plataforma de Stewart acionada hidraulicamente. O modelo dinâmico não linear da plataforma de Stewart com seis graus de liberdade foi desenvolvido no ambiente de sistemas multicorpos ADAMS. Este pacote comercial foi usado para economizar tempo e esforço na modelagem de um sistema mecânico complexo e na programação para obter a resposta no tempo do sistema. A plataforma de Stewart é um manipulador paralelo com alta relação força-peso e acuracidade de posicionamento comparada a manipuladores seriais convencionais. As desvantagens dos mecanismos seriais é que cada articulação suporta o peso da articulação seguinte e mais o objeto a ser manipulado. A plataforma de Stewart tem recebido recentemente considerável interesse de pesquisadores dado o sucesso de suas aplicações e potencial vantagens sobre os manipuladores convencionais. Uma aplicação bastante popular da plataforma de Stewart é o simulador de vôo onde a plataforma executa movimento com acelerações similares àquelas de uma aeronave. Embora muitas pesquisas na literatura tenham dedicado amplo esforço para cinemática, dinâmica e projeto mecânico de manipuladores baseados em plataforma de Stewart, pouca atenção tem sido dada ao problema de controle deste tipo de manipulador. Um esquema de controle difuso e de redes neurais foi adotado para lidar com as não linearidades, distúrbios e incertezas dos parâmetros, e precisão necessária no posicionamento e orientação da plataforma. Redes neurais artificiais e lógica difusa fornecem um paradigma computacional característico e tem demonstrado resultado para uma faixa de problemas práticos onde a técnica computacional convencional não tem sucesso. Em particular, a habilidade do controle neural e do controle difuso para representar mapeamento não linear encoraja o estudo de controle neural e difuso para representar problemas de controle não linear. Resultados de simulação são apresentados, mostrando que as técnicas propostas podem ser usadas na plataforma de Stewart. / This work presents a neural and fuzzy control design technique for a hydraulically driven Stewart platform. The non-linear dynamic model of a Stewart platform with six degrees of freedom was developed in the multibody systems environment ADAMS. This commercial package was used to save time and effort in modelling the complex mechanical system and in the programming to get the time response of the system. The Stewart platform is a parallel manipulator with high force-to-weight ratio and position accuracy compared to conventional serial manipulators. The disadvantage of serial mechanisms is that each link has to support the weigth of all the following links in addition to the object to be supported. The Stewart platform has recently received considerable research interest due to its successful applications and potential advantages over the conventional manipulators. A quite popular application of the Stewart platform is the flight simulator where the platform performs motion with accelerations similar to those of an airplane. Although much of the research in the literature has devoted extensive effort to the kinematics, dynamics and mechanisms design of the Stewart platform-based manipulators, little attention has been paid to the control problem of this type of manipulators. A fuzzy and neural network control scheme was adopted to deal with the nonlinealities, disturbances and uncertainties of the parameters, and required precision in position and orientation the platform. Artificial neural networks and fuzzy logic provide a distinctive computational paradigm and have proven to be effective for a range of practical problems where conventional computation techniques have not succeeded. In particular, the ability of neural and fuzzy control techniques to represent non-linear mappings encourages the study of these techniques to be used for controling complex non-linear control problems. Simulations results are presented, showing that the proposed technique can be used in a Stewart platform.