Modelling mitral valvular dynamics–current trend and future directions

Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed

3D Neuro-electronic interface devices for neuromuscular control: Design studies and realisation steps

In order to design the shape and dimensions of new 3D multi-microelectrode information transducers properly, i. e. adapted to the scale of information delivery to and from peripheral nerve fibres, a number of studies were, and still are, being performed on modelling and simulation of electrical volume conduction inside and outside nerves, on animal experiments on stimulation and recording with single wires and linear arrays, and on new technologies for 3D micro-fabrication. This paper presents a selection of the results of these `Neurotechnology¿ studies at the University of Twente. The experimental and simulation results apply primarily to the peripheral motor nerves of the rat, but are also of interest for neural interfacing with myelinated nerves in man, as fascicles in man are about the same size as in the rat

Masticatory biomechanics in the rabbit : a multi-body dynamics analysis

Acknowledgement We thank Sue Taft (University of Hull) for the µCT-scanning of the rabbit specimen used in this study. We also thank Raphaël Cornette, Jacques Bonnin, Laurent Dufresne, and l'Amicale des Chasseurs Trappistes (ACT) for providing permission and helping us capture the rabbits used for the in vivo bite force measurements at la Réserve Naturelle Nationale de St Quentin en Yvelines, France.Peer reviewedPublisher PD

Quantitative analysis of single muscle fibre action potentials recorded at known distances

In vivo records of single fibre action potentials (SFAPs) have always been obtained at unknown distance from the active muscle fibre.\ud \ud A new experimental method has been developed enabling the derivation of the recording distance in animal experiments. A single fibre is stimulated with an intracellular micropipette electrode. The same electrode is used thereafter for labelling with an auto-fluorescent dye, Lucifer Yellow. In this method there is no use of chemical fixation. The tissue structure is kept as well as possible. In cross-sections the fluorescent fibre is seen and its position is quantitized with respect to the tip of one or more recording wire electrodes.\ud \ud Morphometric data, such as the recording distance and the fibre cross-sectional area, are used for the interpretation of parameters of the SFAPs (peak-peak amplitude, time between the first positive and negative peaks). The present results show that within 300 μm recording distance is not as dominant for the SFAP shape as expected.\ud \ud The method offers also a direct check of the relation between the muscle fibre; diameter and the conduction velocity of the action potential. In the present small set of data there is no simple linear relationship

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces

Real-time simulation of three-dimensional shoulder girdle and arm dynamics

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development