EXTERNAL A/D CONVERTER USING THE UNIVERSAL SERIAL BUS (USB)

A versatile low cost analog to digital conversion unit built from commercially available components that connects to a standard PC or Notebook via the Universal Serial Bus was developed. With a sampling rate of 10kHz, 8 bit resolution and the potential for a large number of channels the system can be used for analog data acquisition in research, classroom and field environments. The A/D converter has been successfully used for the testing of gymnastics landing sunaces

QUANTIFICATION OF STABILISING BEHAVIOUR ON A 2-DOF PLATFORM

A low cost, reliable system that allows for quantification of the stabilising behaviour of subjects on a POSTUROMEO platform was developed. This was achieved with an ordinary mouse connected to the RS232 interface of a standard PC. The system can be set up and used within minutes. The modular structure of the software facilitates customisation to a variety of measurement protocols. The system is well suited to quantify stabilising behaviour with an maximum relative error of less than 2 %

Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping

In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.Comment: 10 pages, 4 figures, 1 table, 5 algorithm

External control strategies for self-propelled particles: optimizing navigational efficiency in the presence of limited resources

We experimentally and numerically study the dependence of different navigation strategies regarding the effectivity of an active particle to reach a predefined target area. As the only control parameter, we vary the particle's propulsion velocity depending on its position and orientation relative to the target site. By introducing different figures of merit, e.g. the time to target or the total consumed propulsion energy, we are able to quantify and compare the efficiency of different strategies. Our results suggest, that each strategy to navigate towards a target, has its strengths and weaknesses and none of them outperforms the other in all regards. Accordingly, the choice of an ideal navigation strategy will strongly depend on the specific conditions and the figure of merit which should be optimized

Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods

Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. Based on a recent idea of stabilization of such algorithms in the scalar output case, we here consider the vectorial extension built on VKOGA. We introduce the so called $\gamma$-restricted VKOGA, comment on analytical properties and present numerical evaluation on data from a clinically relevant application, the modelling of the human spine. The experiments show that the new stabilized algorithms result in improved accuracy and stability over the non-stabilized algorithms

DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems

Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, however, has not been able to show similar performance. We conjecture that ineffective exploration in large overactuated action spaces is a key problem. This is supported by the finding that common exploration noise strategies are inadequate in synthetic examples of overactuated systems. We identify differential extrinsic plasticity (DEP), a method from the domain of self-organization, as being able to induce state-space covering exploration within seconds of interaction. By integrating DEP into RL, we achieve fast learning of reaching and locomotion in musculoskeletal systems, outperforming current approaches in all considered tasks in sample efficiency and robustness

WEEKLY DEVELOPMENT OF FUNCTIONAL QUADRICEPS STRENGTH PARAMETERS DURING HIGH-INTENSITY RESISTANCE TRAINING

The time course of muscular adaptation is unclear, especially for strength parameters measured by interpolated twitch technique, such as peak twitch torque, voluntary activation level and rate of toque development. Two male subjects participated in a longitudinal study over 11 weeks, with one pre-measurement, eight weeks of training and two weeks of detraining (rest). Resting twitch parameters decreased for both participants and only recovered to baseline level and above in the detraining phase. Voluntary quadricsps strength increased with training, with increasing activation level. The study shows the complexity of adaptation to intense strength training, being influenced by fatigue and individual factors and showing the need for a careful consideration of resistance training intensity in athletes prior to competition or scientific settings

The Benefit of Combining Neuronal Feedback and Feed-Forward Control for Robustness in Step Down Perturbations of Simulated Human Walking Depends on the Muscle Function

It is often assumed that the spinal control of human locomotion combines feed-forward central pattern generation with sensory feedback via muscle reflexes. However, the actual contribution of each component to the generation and stabilization of gait is not well understood, as direct experimental evidence for either is difficult to obtain. We here investigate the relative contribution of the two components to gait stability in a simulation model of human walking. Specifically, we hypothesize that a simple linear combination of feedback and feed-forward control at the level of the spinal cord improves the reaction to unexpected step down perturbations. In previous work, we found preliminary evidence supporting this hypothesis when studying a very reduced model of rebounding behaviors. In the present work, we investigate if the evidence extends to a more realistic model of human walking. We revisit a model that has previously been published and relies on spinal feedback control to generate walking. We extend the control of this model with a feed-forward muscle activation pattern. The feed-forward pattern is recorded from the unperturbed feedback control output. We find that the improvement in the robustness of the walking model with respect to step down perturbations depends on the ratio between the two strategies and on the muscle to which they are applied. The results suggest that combining feed-forward and feedback control is not guaranteed to improve locomotion, as the beneficial effects are dependent on the muscle and its function during walking