Control strategies for integration of electric motor assist and functional electrical stimulation in paraplegic cycling: Utility for exercise testing and mobile cycling

AIM: The aim of this study was to investigate feedback control strategies for integration of electric motor assist and functional electrical stimulation (FES) for paraplegic cycling, with particular focus on development of a testbed for exercise testing in FES cycling, in which both cycling cadence and workrate are simultaneously well controlled and contemporary physiological measures of exercise performance derived. A second aim was to investigate the possible benefits of the approach for mobile, recreational cycling. METHODS: A recumbent tricycle with an auxiliary electric motor is used, which is adapted for paraplegic users, and instrumented for stimulation control. We propose a novel integrated control strategy which simultaneously provides feedback control of leg power output (via automatic adjustment of stimulation intensity) and cycling cadence (via electric motor control). Both loops are designed using system identification and analytical (model-based) feedback design methods. Ventilatory and pulmonary gas exchange response profiles are derived using a portable system for real-time breath-by-breath acquisition. RESULTS:We provide indicative results from one paraplegic subject in which a series of feedback-control tests illustrate accurate control of cycling cadence, leg power control, and external disturbance rejection. We also provide physiological response profiles from a submaximal exercise step test and a maximal incremental exercise test, as facilitated by the control strategy. CONCLUSION: The integrated control strategy is effective in facilitating exercise testing under conditions of well-controlled cadence and power output. Our control approach significantly extends the achievable workrate range and enhances exercise-test sensitivity for FES cycling, thus allowing a more stringent characterization of physiological response profiles and estimation of key parameters of aerobic function.We further conclude that the control approach can significantly improve the overall performance of mobile recreational cycling

A pragmatic multi-centre randomised controlled trial of fluid loading and level of dependency in high-risk surgical patients undergoing major elective surgery : trial protocol

Peer reviewedPublisher PD

Rheological techniques for determining degradation of polylactic acid in bioresorbable medical polymer systems

© 2015 AIP Publishing LLC. A method developed in the 1980s for the conversion of linear rheological data to molar mab distribution is revisited in the context of degradable polymers. The method is first applied using linear rheology for a linear polystyrene, for which all conversion parameters are known. A proof of principle is then carried out on four polycarbonate grades. Finally, preliminary results are shown on degradable polylactides. The application of this method to degrading polymer systems, and to systems containing nanofillers, is also discubed. This work forms part of a wider study of bioresorbable nanocomposites using polylactides, novel hydroxyapatite nanoparticles and tailored dispersants for medical applications

Non-Linear Systems Identification Using Neural Networks

Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems. This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer. New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach

Compounding and rheometry of PLA nanocomposites with coated and uncoated hydroxyapatite nanoplatelets

Polylactic acid and novel nanoplatelets of hydroxyapatite (HANP) were compounded in a laboratory scale twin-screw extruder and injection moulded to shape. The effect of HANP loading content, between 1 wt% and 10 wt%, and of HANP surface coating with tailored molecular dispersants, on the processability and rheological behaviour were investigated. Dispersion of HANP within the matrix system was determined qualitatively using transmission electron micrographs. Surface coating of HANP with dispersants was observed to change the state of HANP dispersion in the nanocomposites. This was also reflected in the changes of the nanocomposites’ rheological response with the moduli of coated HANP systems increasing at lower frequencies

Practical Identification of Narmax Models Using Radial Basis Functions.

A wide class of discrete time non-linear systems can be represented by the non-linear autoregressive moving average model with exogenous inputs or NARMAX model. This paper develops a practical algorithm for identifying NARMAX models based on radial basis functions from noise corrupted data. The algorithm consists of an iterative orthogonal-forward-regression routine coupled with model validity tests. The orthogonal-forward-regression routine selects parsimonious radial-basis-function models while the model validity tests measure the quality of fit. The modelling of a liquid level system and an automotive diesel engine are included to demonstrate the effectiveness of the identification procedure

Non-Linear Systems Identification Using Radial Basis Functions

This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial-basis-function centres from a large number of possible candidates and this provides, for the first time, a fully automatic selection procedure for identifying parsimonious radial-basis-function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach

A Parallel Recursive Prediction Error Algorithm for Training Layered Neural Networks

A new recursive prediction error algorithm is derived for the training of feedforward layered neural networks. The algorithm enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence properties than the classical back-propagation algorithm. The relationship between this new parallel algorithm and other existing learning algorithms is discussed. Examples taken from the fields of communication channel equalisation and non-linear systems modelling are used to demonstrate the superior performance of the new algorithm compared with the back propagation routine