Simulated Robotic Autonomous Agents with Motion Evolution

This research implemented autonomous control of robotic agents. The movement controls are simulated within a virtual environment. The control system algorithms were subjected to evolution. Genetic algorithms were implemented to enable the robotic agents to adapt in response to objects within the virtual environment. Additionally, each robot’s physical characteristics were subjected to evolution through a survival of the fittest system based on crossover with random mutations. Survival of the fittest was simulated by a shortage of food causing competition. When the food quantity was increased the evolution rate decreased. With increased food, there was reduced competition and average fitness stopped increasing over time. Removing the food bottleneck stopped the survival of the fittest mechanism

Swapping algorithm and meta-heuristic solutions for combinatorial optimization n-queens problem

This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and provides data from experimental performance results of this new algorithm. A summary is also provided of various meta-heuristic approaches which have been used to solve the n-queens problem including neural networks, evolutionary algorithms, genetic programming, and recently Imperialist Competitive Algorithm (ICA). Currently the Cooperative PSO algorithm is the best algorithm in the literature for finding the first valid solution. Also the research looks into the effect of the number of hidden nodes and layers within neural networks and the effect on the time taken to find a solution. This paper proposes a new swapping algorithm which swaps the position of queens

Comparing and Combining Time Series Trajectories Using Dynamic Time Warping

This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to enable (i) two trajectories to be compared as a similarity measure and also enables (ii) two or more trajectories to be combined together to produce a typical or representative average trajectory using a novel hierarchical DTW process. Our experiments included 100 expert and 100 novice simulator recordings. The data consists of multivariate time series data-streams including multi-dimensional trajectories combined with force and pressure measurements. Our results show that our proposed application of DTW provides a useful time-independent method for (i) comparing two trajectories by providing a similarity measure and (ii) combining two or more trajectories into one, showing higher performance compared to conventional methods such as linear mean. These results demonstrate that DTW can be useful within virtual reality training simulators to provide a component in an automated scoring and assessment feedback system

Monitoring Rehabilitation Parameters In Stroke Patients

This research presents the development and testing of a system for monitoring functional parameters in stroke patients undergoing rehabilitation. Benefits of real-time automated monitoring will improve measurement consistency and accuracy, reduce consultant time, earlier discharge, less hospital beds required and delivery of controlled, repetitive training. The system uses three devices: (1) the Myo gesture control armband (Thalmic Labs) to detect EMG signals, angles and acceleration; (2) the Arm Motion Monitoring and Recovery Improvement Toolkit (AMMRIT) (custom built) arm exoskeleton to monitor the whole arm angles and (3) the Kinect Sensor (Microsoft) to detect facial expressions. Stroke is the second most common cause of death and the leading cause of disability in Europe. The incidence rate is approximately 16 per 10,000 per year in the UK. One of the most effective treatments following stroke is physiotherapy which can help the patient relearn how to move the limbs. Quantifying the progress of functional recovery is technically complex because of the multi-joined structure of the arm. Currently there is no single portable system available that can provide objective measurement and analysis to monitor functional recovery for Early Supported Discharge (ESD). We developed the AMMRIT which can guide as well as assess the arm’s functional recovery of patients. In this research we combine the device with Myo armband and Kinect sensor to monitor a wide range of functional parameters to accurately assess the rehabilitation progression

Virtual Hip Replacement Simulator For 3D Printed Implants.

This research presents a virtual reality simulator for total hip replacement surgery. The simulator supports a library of 3D hip stem models for different sizes and manufacturers. The 3D hip stems can be adjusted in size and shape by parametric software and sent for 3D printing. Biocompatible materials such as titanium enable the 3D printed stems to be directly implanted on patients. Currently surgical simulation for orthopaedic procedures is not as advanced as other surgical disciplines. As a result there are only limited training simulators available for orthopaedic surgery such as total hip replacement, hip resurfacing or knee replacement. This is demanding since 66,000 hip replacements are performed annually in the UK. One area which is neglected in VR orthopaedic simulation is the digital library generation of implants. Currently orthopaedic surgeons have limited choice in terms of an exact identification of implant specific to patient requirements. We conducted a literature review of orthopaedic training simulators which found no simulators catering for this

Interpreting Ultrasound Images For Accurate Epidural Needle Insertion.

This work presents development and testing of image processing algorithms for the automatic detection of landmarks within ultrasound images. The aim was to automate ultrasound analysis, for use during the process of epidural needle insertion. For epidural insertion, ultrasound is increasingly used to guide the needle into the epidural space. Ultrasound can improve the safety of epidural and was recommended by the 2008 NICE guidelines (National Institute for Health and Care Excellence). Without using ultrasound, there is no way for the anaesthetist to observe the location of the needle within the ligaments requiring the use of their personal judgment which may lead to injury. If the needle stops short of the epidural space, the anaesthetic is ineffective. If the needle proceeds too deep, it can cause injuries ranging from headache, to permanent nerve damage or death. Ultrasound of the spine is particularly difficult, because the complex bony structures surrounding the spine limit the ultrasound beam acoustic windows. Additionally, the important structures for epidural that need to be observed are located deeper than other conventional procedures such as peripheral nerve block. This is why a low frequency, curved probe (2-5 MHz) is used, which penetrates deeper but decreases in resolution. The benefits of automating ultrasound are to enable real-time ultrasound analysis on the live video, mitigate human error, and ensure repeatability by avoiding variation in perception by different users

Interpreting Ultrasound Images For Accurate Epidural Needle Insertion.

This work presents development and testing of image processing algorithms for the automatic detection of landmarks within ultrasound images. The aim was to automate ultrasound analysis, for use during the process of epidural needle insertion. For epidural insertion, ultrasound is increasingly used to guide the needle into the epidural space. Ultrasound can improve the safety of epidural and was recommended by the 2008 NICE guidelines (National Institute for Health and Care Excellence). Without using ultrasound, there is no way for the anaesthetist to observe the location of the needle within the ligaments requiring the use of their personal judgment which may lead to injury. If the needle stops short of the epidural space, the anaesthetic is ineffective. If the needle proceeds too deep, it can cause injuries ranging from headache, to permanent nerve damage or death. Ultrasound of the spine is particularly difficult, because the complex bony structures surrounding the spine limit the ultrasound beam acoustic windows. Additionally, the important structures for epidural that need to be observed are located deeper than other conventional procedures such as peripheral nerve block. This is why a low frequency, curved probe (2-5 MHz) is used, which penetrates deeper but decreases in resolution. The benefits of automating ultrasound are to enable real-time ultrasound analysis on the live video, mitigate human error, and ensure repeatability by avoiding variation in perception by different users

How to use Factiva

Chapter from the Library training course "A guide to Business and Economics databases", this pdf shows students how to search and find newspaper articles in the Factiva database

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Multi-Agent Reinforcement Learning for Swarm Retrieval with Evolving Neural Network

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-95972-6_56This research investigates methods for evolving swarm communica-tion in a sim-ulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn phero-mone communication autonomously. Building on previous literature on phero-mone communication, this research applies evolution to adjust the topology and weights of an artificial neural network (ANN) which controls the ant behaviour. Compar-ison of performance is made between a hard-coded benchmark algorithm (BM1), a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was suc-cessfully evolved to achieve the task objective, to collect food and return it to a location