Staff within the Department of Computer Science have research interests in Visualization, Interaction & Computer Graphics (with a particular focus on Medical Graphics), Cyber Security and Discrete Optimisation.

Recent Submissions

  • Translational Medicine: Challenges and new orthopaedic vision (Mediouni-Model)

    Mediouni, Mohamed; Madiouni, Riadh; Gardner, Michael; Vaughan, Neil; University of Chester, UK
    Background: In North America and three European countries Translational Medicine (TM) funding has taken center stage as the National Institutes of Health (NIH), for example, has come to recognize that delays are common place in completing clinical trials based upon benchside advancements. Recently, there are several illustrative examples whereby the translation of research had untoward outcomes requiring immediate action. Methods: Focus more on three-dimensional (3D) simulation, biomarkers, and Artificial Intelligence may allow orthopaedic surgeons to predict the ideal practices before orthopaedic surgery. Using the best medical imaging techniques may improve the accuracy and precision of tumor resections. Results: This article is directed at the young surgeon scientist and in particular orthopaedic residents and all other junior physicians in training to help them better understand TM and position themselves in career paths and hospital systems that strive for optimal TM. It serves to hasten the movement of knowledge garnered from the benchside and move it quickly to the bedside. Conclusions: Communication is ongoing in a bidirectional format. It is anticipated that more and more medical Centers and institutions will adopt TM models of healthcare delivery.
  • An overview of self-adaptive technologies within virtual reality training

    Vaughan, Neil; Gabrys, Bogdan; Dubey, Venketesh; University of Chester
    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.
  • Modelling the effects of glucagon during glucose tolerance testing.

    Kelly, Ross A; orcid: 0000-0002-5505-460X; email: R.A.Kelly@ljmu.ac.uk; Fitches, Molly J; Webb, Steven D; Pop, S R; Chidlow, Stewart J (2019-12-12)
    BACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted.
  • De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal

    Chen, Long; Tang, Wen; John, Nigel W.; Wan, Tao Ruan; Zhang, Jian Jun; Bournemouth University; University of Chester; University of Bradford (IEEE XPlore, 2019-11-15)
    Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.
  • Policing the Cyber Threat: Exploring the threat from Cyber Crime and the ability of local Law Enforcement to respond

    Eze, Thaddeus; Hull, Matthew; Speakman, Lee; University of Chester (Proceedings of the IEEE, 2019-07-01)
    The landscape in which UK policing operates today is a dynamic one, and growing threats such as the proliferation of cyber crime are increasing the demand on police resources. The response to cyber crime by national and regional law enforcement agencies has been robust, with significant investment in mitigating against, and tackling cyber threats. However, at a local level, police forces have to deal with an unknown demand, whilst trying to come to terms with new crime types, terminology and criminal techniques which are far from traditional. This paper looks to identify the demand from cyber crime in one police force in the United Kingdom, and whether there is consistency in the recording of crime. As well as this, it looks to understand whether the force can deal with cyber crime from the point of view of the Police Officers and Police Staff in the organisation.
  • Assisting Serious Games Level Design with an Augmented Reality Application and Workflow

    Beever, Lee; John, Nigel W.; Pop, Serban R.; University of Chester (Eurographics Proceedings, 2019-09-13)
    With the rise in popularity of serious games there is an increasing demand for virtual environments based on real-world locations. Emergency evacuation or fire safety training are prime examples of serious games that would benefit from accurate location depiction together with any application involving personal space. However, creating digital indoor models of real-world spaces is a difficult task and the results obtained by applying current techniques are often not suitable for use in real-time virtual environments. To address this problem, we have developed an application called LevelEd AR that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability. Finally, we carried out a preliminary case study that demonstrates how LevelEd AR can be successfully used as part of current industry workflows for serious games level design.
  • Context-Aware Mixed Reality: A Learning-based Framework for Semantic-level Interaction

    Chen, Long; Tang, Wen; Zhang, Jian Jun; John, Nigel W.; Bournemouth University; University of Chester; University of Bradford
    Mixed Reality (MR) is a powerful interactive technology for new types of user experience. We present a semantic-based interactive MR framework that is beyond current geometry-based approaches, offering a step change in generating high-level context-aware interactions. Our key insight is that by building semantic understanding in MR, we can develop a system that not only greatly enhances user experience through object-specific behaviors, but also it paves the way for solving complex interaction design challenges. In this paper, our proposed framework generates semantic properties of the real-world environment through a dense scene reconstruction and deep image understanding scheme. We demonstrate our approach by developing a material-aware prototype system for context-aware physical interactions between the real and virtual objects. Quantitative and qualitative evaluation results show that the framework delivers accurate and consistent semantic information in an interactive MR environment, providing effective real-time semantic level interactions.
  • Evaluating LevelEd AR: An Indoor Modelling Application for Serious Games Level Design

    Beever, Lee; Pop, Serban R.; John, Nigel W.; University of Chester (IEEE Conference Publications, 2019-09-06)
    We developed an application that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone to assist with serious games level design. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability.
  • Virtual Reality Environment for the Cognitive Rehabilitation of Stroke Patients

    John, Nigel W.; Day, Thomas W.; Pop, Serban R.; Chatterjee, Kausik; Cottrell, Katy; Buchanan, Alastair; Roberts, Jonathan; University of Chester; Countess of Chester Hospital NHS Foundation Trust; Cadscan Ltd (IEEE, 2019-09-04)
    We present ongoing work to develop a virtual reality environment for the cognitive rehabilitation of patients as a part of their recovery from a stroke. A stroke causes damage to the brain and problem solving, memory and task sequencing are commonly affected. The brain can recover to some extent, however, and stroke patients have to relearn to carry out activities of daily learning. We have created an application called VIRTUE to enable such activities to be practiced using immersive virtual reality. Gamification techniques enhance the motivation of patients such as by making the level of difficulty of a task increase over time. The design and implementation of VIRTUE is presented together with the results of a small acceptability study.
  • Training Powered Wheelchair Manoeuvres in Mixed Reality

    Day, Thomas W.; John, Nigel W.; University of Chester (IEEE Xplore, 2019-09)
    We describe a mixed reality environment that has been designed as an aid for training driving skills for a powered wheelchair. Our motivation is to provide an improvement on a previous virtual reality wheelchair driving simulator, with a particular aim to remove any cybersickness effects. The results of a validation test are presented that involved 35 able bodied volunteers divided into three groups: mixed reality trained, virtual reality trained, and a control group. No significant differences in improvement was found between the groups but there is a notable trend that both the mixed reality and virtual reality groups improved more than the control group. Whereas the virtual reality group experienced discomfort (as measured using a simulator sickness questionnaire), the mixed reality group experienced no side effects.
  • An overview of thermal necrosis: present and future

    Mediouni, M.; Kucklick, T.; Poncet, S.; Madiouni, R.; Abouaomar, A.; Madry, H.; Cucchiarini, M.; Chopko, B.; Vaughan, Neil; Arora, M.; et al. (Taylor & Francis, 2019-05-10)
    Introduction: Many orthopaedic procedures require drilling of bone, especially fracture repair cases. Bone drilling results in heat generation due to the friction between the bone and the drill bit. A high-level of heat generation kills bone cells. Bone cell death results in resorption of bone around bone screws. Materials and methods: We searched in the literature for data on parameters that influence drilling bone and could lead to thermal necrosis. The points of view of many orthopaedists and neurosurgeons based upon on previous practices and clinical experience are presented. Results: Several potential complications are discussed and highlighted that lead to thermal necrosis. Discussion: Even in the face of growing evidence as to the negative effects of heat-induction during drilling, simple and effective methods for monitoring and cooling in real-time are not in widespread usage today. For that purpose, we propose some suggestions for the future of bone drilling, taking note of recent advances in autonomous robotics, intelligent systems, and computer simulation techniques. Conclusions: These advances in prevention of thermal necrosis during bone drilling surgery are expected to reduce the risk of patient injury and costs for the health service.
  • Adapting Jake Knapp’s Design Sprint Approach for AR/VR Applications in Digital Heritage

    Southall, Helen; Marmion, Maeve; Davies, Andrew; University of Chester (Springer Nature, 2019-04-21)
    Modern digital devices offer huge potential for the delivery of engaging heritage experiences to visitors, offering a better visitor experience, higher visitor numbers, and opportunities for increased tourism income. However, all software development entails risk, including the risk of developing a product which few will want, or be able, to use. Identifying user experience priorities and problems at an early stage is therefore extremely important. This chapter describes work in progress on a shortened version of Jake Knapp’s Design Sprint approach, and its application to designing VR/AR solutions for a specific heritage case study.
  • Colour Coded Emotion Classification in Mental Health Social Media

    Vaughan, Neil; Mulvenna, Maurice; Bond, Raymond; Royal Academy of Engineering; University of Chester (BCS, The Chartered Institute for IT, ACM Proceedings, 2018-07-06)
    This research applies emotion detection to messages from online mental health social media. In particular, this focusses on specialised social media for users to report health or mental health problems. Automatically detecting the emotion in social media can help to rapidly identify any concerning problems which could benefit from intervention aiming to prevent self-harming or suicide. Detecting emotions enables messages to be colour coordinated according to the emotion to enhance the human-computer interaction. A supervised classification method is applied to a labelled dataset and results presented. A prototype user interface system is developed based on detecting emotion, colour coding the message to display detected emotions to users in real-time.
  • VRIA - A Framework for Immersive Analytics on the Web

    Butcher, Peter; John, Nigel W.; Ritsos, Panagiotis D.; University of Chester and Bangor University (ACM, 2019-05)
    We report on the design, implementation and evaluation of <VRIA>, a framework for building immersive analytics (IA) solutions inWeb-based Virtual Reality (VR), built upon WebVR, A-Frame, React and D3. The recent emergence of affordable VR interfaces have reignited the interest of researchers and developers in exploring new, immersive ways to visualize data. In particular, the use of open-standards web-based technologies for implementing VR in a browser facilitates the ubiquitous and platform-independent adoption of IA systems. Moreover, such technologies work in synergy with established visualization libraries, through the HTML document object model (DOM). We discuss high-level features of <VRIA> and present a preliminary user experience evaluation of one of our use-cases.
  • Appearance Modeling of Living Human Tissues

    Maciel, Anderson; Meyer, Gary W.; John, Nigel W.; Walter, Marcelo; Nunes, Augusto L. P.; Baranoski, Gladimir V. G.; Federal Institute of Paraná, Londrina; Universidade Federal do Rio Grande do Sul; University of Minnesota; University of Chester; University of Waterloo (Wiley, 2019-02-27)
    The visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modeling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas.
  • Towards Organisational Learning Enhancement: Assessing Software Engineering Practice

    Fannoun, Sufian; Kerins, John; University of Chester (Emerald Publishing Limited, 2018-12-17)
    • Purpose – Issues surrounding knowledge management, knowledge transfer and learning within organisations challenge continuity and resilience in the face of changing environments. While initiatives are principally applied within large organisations, there is scope to assess how the processes are handled within small and medium enterprises (SMEs) and to consider how they might be enhanced. This paper presents an evaluation of practice within an evolving software development unit to determine what has been learned and how the knowledge acquired has been utilised to further organisational development. These results provide the basis for the design and implementation of a proposed support tool to enhance professional practice. • Design/methodology/approach – A small software development unit, which has successfully delivered bespoke systems since its establishment a number of years ago, was selected for analysis. The unit operates as a team whose actions and behaviours were identified and validated by the following means: in-depth interviews were carried out with each member of the team to elicit an understanding of individual and collective development. Interview data were recorded and transcribed and subjected to qualitative analysis to identify key themes underpinning knowledge acquisition and utilisation. Samples of project documentation were scrutinised to corroborate interview data. After analysing the data, a focus-group meeting was held to validate the results and to generate further insights into learning within the team. • Findings - Qualitative analysis of the data revealed key changes in thinking and practice within the team as well as insight into the development of individual and collective contextual knowledge, tacit understanding and learning. This analysis informed the proposal of a bespoke, lightweight, web-based system to support knowledge capture and organisational learning (OL). This approach has the potential to promote resilience and to enhance practice in similar small or start-up enterprises. • Research limitations/implications – Purposeful sampling was used in selecting a small software development team. This enabled in-depth interviewing of all members of the team. This offered a rich environment from which to derive awareness and understanding of individual and collective knowledge acquisition and learning. Focusing on a single small enterprise limits the extent to which the findings can be generalised. However, the research provides evidence of effective practice and learning and has identified themes for the development of a support tool. This approach can be extended to similar domains to advance research into learning and development. • Practical implications – Results of the work undertaken so far have generated promising foundations for the proposed support tool. This offers software developers a system within which they can reflect upon, and record, key learning events affecting technical, managerial and professional practice. • Originality/value – Small enterprises have limited resources to support OL. The qualitative research undertaken so far has yielded valuable insight into the successful development of a single software development team. The construction of a support tool to enhance knowledge acquisition and learning has the capacity to consolidate valuable, and potentially scarce, expertise. It also has the potential to facilitate further research to determine how the prototype might be extended or revised to improve its contribution to the team’s development.
  • Evolutionary Robot Swarm Cooperative Retrieval

    Vaughan, Neil; Royal Academy of Engineering; University of Chester (Springer, 2018-07-07)
    In nature bees and leaf-cutter ants communicate to improve cooperation during food retrieval. This research aims to model communication in a swarm of auton-omous robots. When food is identified robot communication is emitted within a limited range. Other robots within the range receive the communication and learn of the location and size of the food source. The simulation revealed that commu-nication improved the rate of cooperative food retrieval tasks. However a counter-productive chain reaction can occur when robots repeat communications from other robots causing cooperation errors. This can lead to a large number of robots travelling towards the same food source at the same time. The food becomes de-pleted, before some robots have arrived. Several robots continue to communicate food presence, before arriving at the food source to find it gone. Nature-inspired communication can enhance swarm behaviour without requiring a central control-ler and may be useful in autonomous drones or vehicles.
  • Evaluating current practice and proposing a system to enhance knowledge assets within a small software development unit

    Fannoun, Sufian; Kerins, John; The University of Chester (IEEE, 2018-06-25)
    Knowledge management and knowledge transfer within organisations challenge continuity and resilience in the face of changing environments. While issues are principally addressed within large organisations, there is scope to evaluate how knowledge assets are managed within small and medium enterprises and to consider how the process might be enhanced. The research reported here aimed to evaluate practice within an evolving software development unit to understand how knowledge has been acquired and utilised to further organisational development. In-depth interviews were carried out with members of the unit to elicit an understanding of individual and collective learning. Qualitative analysis of the data revealed key changes in thinking and practice as well as insight into the development of individuals' contextual knowledge and tacit understanding. This analysis led to the proposal of a bespoke, lightweight web-based system to support knowledge capture and organisational learning. This work is still in progress but it is anticipated that the results will provide a potentially novel and beneficial method for enhancing knowledge assets in small enterprises and consolidating valuable, and potentially scarce, expertise.
  • Evolution of Neural Networks for Physically Simulated Evolved Virtual Quadruped Creatures

    Vaughan, Neil; Royal Academy of Engineering; University of Chester (Springer-Verlag, 2018-07-07)
    This work develops evolved virtual creatures (EVCs) using neuroevolution as the controller for movement and decisions within a 3D physics simulated environ-ment. Previous work on EVCs has displayed various behaviour such as following a light source. This work is focused on complexifying the range of behaviours available to EVCs. This work uses neuroevolution for learning specific actions combined with other controllers for making higher level decisions about which action to take in a given scenario. Results include analysis of performance of the EVCs in simulated physics environment. Various controllers are compared including a hard coded benchmark, a fixed topology feed forward artificial neural network and an evolving ANN subjected to neuroevolution by applying mutations in both topology and weights. The findings showed that both fixed topology ANNs and neuroevolution did successfully control the evolved virtual creatures in the distance travelling task.
  • Swarm Communication by Evolutionary Algorithms

    Vaughan, Neil; University of Chester (IEEE, 2018-05-27)
    This research has applied evolutionary algorithms to evolve swarm communication. Controllers were evolved for colonies of artificial simulated ants during a food foriaging task which communicate using pheromone. Neuroevolution enables both weights and the topology of the artificial neural networks to be optimized for food foriaging. The developed model results in evolution of ants which communicate using pheromone trails. The ants successfully collect and return food to the nest. The controller has evolved to adjust the strength of pheromone which provides a signal to guide the direction of other ants in the colony by hill climbing strategy. A single ANN controller for ant direction successfully evolved which exhibits many separate skills including food search, pheromone following, food collection and retrieval to the nest.

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