Design and Validation of a Bimanual Haptic Epidural Needle Insertion Simulator

The case experience of anesthesiologists is one of the leading causes of accidental dural puncture and failed epidural - the most common complications of epidural analgesia. We designed a bimanual haptic simulator to train anesthesiologists and optimize epidural analgesia skill acquisition, and present a validation study conducted with 15 anesthesiologists of different competency levels from several hospitals in Israel. Our simulator emulates the forces applied on the epidural (Touhy) needle, held by one hand, and those applied on the Loss of Resistance (LOR) syringe, held by the second hand. The resistance is calculated based on a model of the Epidural region layers that is parameterized by the weight of the patient. We measured the movements of both haptic devices, and quantified the rate of results (success, failed epidurals and dural punctures), insertion strategies, and answers of participants to questionnaires about their perception of the realism of the simulation. We demonstrated good construct validity by showing that the simulator can distinguish between real-life novices and experts. Good face and content validity were shown in experienced users' perception of the simulator as realistic and well-targeted. We found differences in strategies between different level anesthesiologists, and suggest trainee-based instruction in advanced training stages.Comment: 12 pages, 11 figure

An investigation of genetic operators for continuous parameter space

The success of a genetic optimization algorithm in continuous parameter space depends on the recombination (crossover) operators that it uses. In this paper we consider a wide spectrum of such operators within a unified framework and study their relative importance in the search process. We consider four basic types recombination operators which cover the relevant exploration potential of a continuous space: Interpolation, Extrapolation, Exchange and Mutation. Each of these basic types may have several variants. We characterize the various operators and their variants by their spatial sampling properties and examine their contributions to the search by applying different mixtures of the operators in several benchmark problems. The results suggest that the optimal mixture of operators may depend on the problem. But, in general, all basic types are needed for efficient optimization

Improved orthogonal array based simulated annealing for design optimization

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated

An analysis of the local optima storage capacity of Hopfield network based fitness function models

A Hopfield Neural Network (HNN) with a new weight update rule can be treated as a second order Estimation of Distribution Algorithm (EDA) or Fitness Function Model (FFM) for solving optimisation problems. The HNN models promising solutions and has a capacity for storing a certain number of local optima as low energy attractors. Solutions are generated by sampling the patterns stored in the attractors. The number of attractors a network can store (its capacity) has an impact on solution diversity and, consequently solution quality. This paper introduces two new HNN learning rules and presents the Hopfield EDA (HEDA), which learns weight values from samples of the fitness function. It investigates the attractor storage capacity of the HEDA and shows it to be equal to that known in the literature for a standard HNN. The relationship between HEDA capacity and linkage order is also investigated

Structure Discovery in Mixed Order Hyper Networks

Background  Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be used for regression, classification or as content addressable memories and have been shown to be useful as fitness function models in constraint satisfaction tasks. They are fast to train and, when their structure is fixed, do not suffer from local minima in the cost function during training. However, their main drawback is that the correct structure (which neurons to connect with weights) must be discovered from data and an exhaustive search is not possible for networks of over around 30 inputs.  Results  This paper presents an algorithm designed to discover a set of weights that satisfy the joint constraints of low training error and a parsimonious model. The combined structure discovery and weight learning process was found to be faster, more accurate and have less variance than training an MLP.  Conclusions  There are a number of advantages to using higher order weights rather than hidden units in a neural network but discovering the correct structure for those weights can be challenging. With the method proposed in this paper, the use of high order networks becomes tractable

Genetic algorithms for order dependent processes applied to robot path-planning

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