Peter Dabnichki

Original article: Unsteady fluid mechanics effects in water based human locomotion

Computational fluid mechanics (CFD) has made substantial progress on modelling a variety of important problems in industry. However, there is still lack of reliable methods to model the motion of the body in water. This is a central issue in understanding animal and human propulsion in water not only to advance science but to explore the possibility of utilising such propulsion modes for man made vehicles. The presented work identified the added mass effect as the prime contributor to propulsive force generation. The use of boundary element method (BEM) proved very successful as it allowed reducing this dynamic problem to a quasi-static one without sacrificing accuracy in the model. The comparison between the experimental data and the simulation result was in the range of 95% (average accuracy) suggesting that the added mass effect and dynamic lift and drag are the most significant physical phenomena in propulsive force generation despite the fact that there is undoubtedly and the presence of turbulent effects that were not considered.

Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral

The speed and pen-pressure while sketching a spiral are lower among Parkinson’s disease (PD) patients with higher severity of the disease. However, the correlation between these features and the severity level (SL) of PD has been reported to be 0.4. There is a need for identifying parameters with a stronger correlation for considering this for accurate diagnosis of the disease. This study has proposed the use of the Composite Index of Speed and Pen-pressure (CISP) of sketching as a feature for analyzing the severity of PD. A total of 28 control group (CG) and 27 PD patients (total 55 participants) were recruited and assessed for Unified Parkinson’s Disease Rating Scale (UPDRS). They drew guided Archimedean spiral on an A3 sheet. Speed, pen-pressure, and CISP were computed and analyzed to obtain their correlation with severity of the disease. The correlation of speed, pen-pressure, and CISP with the severity of PD was −0.415, −0.584, and −0.641, respectively. Mann–Whitney U test confirmed that CISP was suitable to distinguish between PD and CG, while non-parametric k-sample Kruskal–Wallis test confirmed that it was significantly different for PD SL-1 and PD SL-3. This shows that CISP during spiral sketching may be used to differentiate between CG and PD and between PD SL-1 and PD SL-3 but not SL-2.

Modelling, Computing and Sport

This article presents a computational model for propulsion generation in front crawl swimming. The obtained results show close agreement with directly measured forces acting on a computer controlled robotic arm that kinematically simulated a planar arm stroke. The model was applied to real-life swimming strokes and demonstrated that the three-dimensionality improves efficiency of the stroke and that contrary to popular belief the arm does not need to be fully extended while swimming.

An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

Display Omitted Neural Networks were created to predict core and local skin temperatures using a large gender-balanced experimental dataset.NNs significantly increase accuracy with respect to multi-linear regression models (e.g. R-value increase 81% for core).Core temperature is not practical neither required for the prediction of skin temperature using Neural Networks.The addition of Average clothing as an input is beneficial for the prediction of core, forehead and hands temperature.The best predictive models were found for skin temperature at hands and knees. Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets.This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations.A range of Neural Networks were developed for each local temperature, with topologies of 1-2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ź0.46°C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ź1.2°C, which is similar to the precision of existent average skin temperature models even though the average value is more stable.

Note on the Physical Basis of the Kutta Condition in Unsteady Two-Dimensional Panel Methods

Force generation in avian and aquatic species is of considerable interest for possible engineering applications. The aim of this work is to highlight the theoretical and physical foundations of a new formulation of the unsteady Kutta condition, which postulates a finite pressure difference at the trailing edge of the foil. The condition, necessary to obtain a unique solution and derived from the unsteady Bernoulli equation, implies that the energy supplied for the wing motion generates trailing-edge vortices and their overall effect, which depends on the motion initial parameters, is a jet of fluid that propels the wing. The postulated pressure difference (the value of which should be experimentally obtained) models the trailing-edge velocity difference that generates the thrust-producing jet. Although the average thrust values computed by the proposed method are comparable to those calculated by assuming null pressure difference at the trailing edge, the latter (commonly used) approach is less physically meaningful than the present one, as there is a singularity at the foil trailing edge. Additionally, in biological applications, that is, for autonomous flapping, the differences ought to be more significant, as the corresponding energy requirements should be substantially altered, compared to the studied oscillatory motions.

An analytical method for reconstruction of biological objects from discrete noisy data

The work represents a stage in the development of an integrated process for the analysis of biological objects – analytical image reconstruction from noisy data that allows fast dynamical analysis. The method also provides an interface to discrete methods such as finite element method (FEM). Two different methods are proposed – one is based on theta functions and the other uses analytical ellipsoids. Both methods possess built-in ability to remove noise from experimental measurements. The methods also have significant advantages if used in biological applications as it could process data directly from optical or general image devices such as cameras, microscopes and scans. Real-time online reconstruction and relevant computational analysis could be performed due to the rapid computational speed which in turn provides a good opportunity for the development of an integrated medical diagnostics technology. Both methods are demonstrated using appropriate examples.

Computer analysis of bobsleigh team push

Bobsleigh start is a simple action requiring the crew to push as hard as possible and gain maximum initial velocity of the sled at the start. However, detailed computer analysis based on velocity and acceleration data shows that timing of loading play a very important role. In this work we demonstrate that a very important performance parameter commonly called exit velocity can be use as both target and performance measurement. The analysis of time profiles allowed us to modify the timing of the loading and gain nearly 1 km/h on the exit speed.

DinamapBP-automatic data acquisition system for clinical trials

The work presents a data acquisition system for blood pressure measurement in clinical trials based on pervasive computing approach. The system is devised with a view to minimise and probably eradicate input of erroneous data along with a number of other security measures.