Yes / Background: Individuals with lower-limb amputation can use running specific prostheses (RSP) that store and then return elastic energy during stance. However, it is unclear whether varying the stiffness category of the same RSP affects spring-mass behaviour during self-selected, submaximal speed running in individuals with unilateral transtibial amputation. Research question: The current study investigates how varying RSP stiffness affects limb stiffness, running performance, and associated joint kinetics in individuals with a unilateral transtibial amputation. Methods: Kinematic and ground reaction force data were collected from eight males with unilateral transtibial amputation who ran at self-selected submaximal speeds along a 15 m runway in three RSP stiffness conditions; recommended habitual stiffness (HAB) and, following 10-minutes of familiarisation, stiffness categories above (+1) and below (-1) the HAB. Stance-phase centre of mass velocity, contact time, limb stiffness’ and joint/RSP work were computed for each limb across RSP stiffness conditions. Results: With increased RSP stiffness, prosthetic limb stiffness increased, whilst intact limb stiffness decreased slightly (p

In this thesis we present a computational study of “ion crystals”, the interesting patterns in which ions arrange themselves in ion traps such as Paul and Penning traps. In ion crystals the ions are in equilibrium due to the balance of the repulsive forces between the ions and the overall tendency of the ion trap to pull ions towards the trap centre. We have carried out a detailed investigation of ion crystals in Paul traps by solving their equations of motion numerically. We also propose a model called the spring–mass model to explain the formation of ion crystals. This model is far more efficient than direct numerical simulation for predicting ion crystal structures. Finally, we demonstrate that there is a power law relating distance of an ion from the trap centre in ion crystals to the applied RF voltage amplitude.

We present a novel control strategy for running which is robust to disturbances, and makes excellent use of passive dynamics for energy economy. The motivation for our control strategy is based on observations of animals, which are able to economically walk and run over varying terrain and ground dynamics. It is well-known that steady-state animal running can be approximated by spring-mass models, but these passive dynamic models describe only steady-state running and are sensitive to disturbances that animals can accommodate. While animals rely on their passive dynamics for energy economy, they also incorporate active control for disturbance rejection. The same approach can be used for spring-mass walking and running, but an active controller is needed that interferes minimally with the passive dynamics of the system. We demonstrate, in simulation, how force control combined with a leg spring stiffness tuned for the desired hopping frequency provides robustness to disturbances on a model for robot hopping, while maintaining the energy economy of a completely passive system during steady-state operation. Our strategy is promising for robotics applications, because there is a clear distinction between the passive dynamic behavior of the model and the active controller, it does not require sensing of the environment, and it is based on a sound theoretical background that is compatible with existing high-level controllers for ideal spring-mass models. / Graduation date: 2012

Every movement has a goal. For reaching, the goal is to move the hand to a specific location. For locomotion, however, goals for each step cycle are unclear and veiled by the automatic nature of lower limb control. What mechanical variables does the nervous system "care" about during locomotion? Abundant evidence from the biomechanics literature suggests that the force generated on the ground, or endpoint force, is an important task variable during hopping and running. Hopping and running are called bouncing gaits for the reason that the endpoint force trajectory is like that of bouncing on a pogo stick. In this work, I captured kinematics and kinetics of human bouncing gaits, and tested whether structure in the inherent step-to-step variability is consistent with control of endpoint force. I found that joint torques covary from step to step to stabilize only peak force. When two limbs are used to generate force on the ground at the same time, individual forces of the limbs are not stabilized, but the total peak force is stabilized. Moreover, passive dynamics may be exploited during forward progression. These results suggest that the number of kinetic goals is minimal, and this simple control scheme involves goals for discrete times during the gait cycle. Uncovering biomechanical goals of locomotion provides a functional context for understanding how complex joints, muscles, and neural circuits are coordinated.

Aircraft design must be lightweight and cost-efficient on the condition of aircraft certification. In addition to standard load cases, human-induced loads can occur in the aircraft interior. These are crucial for optimal design but difficult to estimate. In this study, a simple bipedal spring-mass model with roller feet predicted human-induced loads caused by human gait for use within an end-to-end design process. The prediction needed no further experimental data. Gait movement and ground reaction force (GRF) were simulated by means of two parameter constraints with easily estimable input variables (gait speed, body mass, body height). To calibrate and validate the prediction model, experiments were conducted in which 12 test persons walked in an aircraft mock-up under different conditions. Additional statistical regression models helped to compensate for bipedal model limitations. Direct regression models predicted single GRF parameters as a reference without a bipedal model. The parameter constraint with equal gait speed in experiment and simulation yielded good estimates of force maxima (error 5.3%), while equal initial GRF gave a more reliable prediction. Both parameter constraints predicted contact time very well (error 0.9%). Predictions with the bipedal model including full GRF curves were overall as reliable as the reference.