The metabolic cost of carrying an additional mass at different locations on the body relates to many areas, such as ergonomics, the military, obesity, and the design of prosthetic and powered exoskeletons devices. It was found that the most important factors that affect change in energy expenditure are the speed of locomotion , the magnitude of the additional mass, and the location of the additional mass on the body . It was suggested that metabolic cost increases linearly as the speed increases, and that it also in-creases linearly as the mass carried is increased. However, other researchers depicted these relations as nonlinear. Further, to the best of our knowledge the combined effect of the weight speed has not been studied. Yet for practical applications it is important to be able to predict the metabolic cost for any given combination of speed and mass, which has not been possible from previous studies. Therefore, in this study we aim to model the change in metabolic cost as a function of walking speed and the additional mass, at three body locations: the back, the knee, and the ankle. This has been done by a meta-analysis of previous published studies.

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Performing closed-loop modifications of a brainmachine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.

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Robotic-powered exoskeletons and body joint-adapted assistive units are currently under development for the enhancement of human locomotor performance in the military, in industries, and in patients and the elderly with mobility impairments. They free people from much labor and the burdens of many kinds of manual work. For example, when it comes to automation in the industrial field, factory automation has made good progress. Operators (humans) can be included in a conventional manufacturing process with respect to a formal production line and uniform working conditions. Automation outside the production line, however, especially in common manufacturing stages, has several limitations and difficulties in adapting to actual conditions because the industrial field has but a small part in the process due to its operating characteristics. There have been many approaches to the reduction of labor that do not only fully assist but also partly aid workers, such as in the use of extremely heavy payload-oriented construction equipment, which are manipulated by humans.

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En este trabajo se presenta el diseño e implementación de un dispositivo de rehabilitación de miembro superior para lesiones del Sistema Nervioso Periférico o Muscular. Éste se sujeta al brazo del paciente mediante una férula que se adapta al tamaño del mismo, y en una primera etapa se ha realizado de manera tal que permita realizar movimientos de flexo-extensión. El prototipo está compuesto por una sección fija que sostiene todo el peso del brazo y una sección móvil, que permite realizar movimientos de flexión y extensión de antebrazo sobre el brazo. El sistema es controlado por medio de una interfaz de usuario, a través de un software llamado RehabI, utilizando MatLab como entorno de programación. Este programa conecta al usuario con el prototipo enviando las órdenes de movimiento al dispositivo a través del puerto de salida de la placa de audio de una computadora personal, pudiendo modificarse la terapia que debe realizar el paciente. Adicionalmente, se puede programar la velocidad y el ángulo de flexión que el paciente debe realizar de acuerdo a las limitaciones que posea en sus movimientos.

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I spent the summer creating a virtual environment that could be integrated for use with our exoskeleton arms. I did so using an open source graphics platform called Coin3D. The code I have written can be found in the Visual Studio Projects folder on the main VR computer. This is located in My Documents -> Visual Studio 2008 in Levi’s account. Developers wishing to create games using this environment or add to the code I have already written can skip straight to “The simulation.” However, the other sections contain information that may be relevant to anyone wishing to modify the existing code.

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