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Faculty of Physiotherapy at University 21, University of Applied Sciences, Germany

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20 Papers

Evaluation of Generative Adversarial Network Generated Super Resolution Images for Micro Expression Recognition

Topics: Feature Selection and Extraction; Image and Video Analysis and Understanding

Authors: Pratikshya Sharma ¹ , Sonya Coleman ¹ , Pratheepan Yogarajah ¹ , Laurence Taggart ² and Pradeepa Samarasinghe ³

Affiliations: ¹ School of Computing, Engineering & Intelligent Systems, Ulster University, Northern Ireland, U.K. , ² School of Nursing & Health Research, Ulster University, Northern Ireland, U.K. , ³ Department of Information Technology, Malabe, Sri Lanka

Digital Object Identifier: 10.5220/0010820100003122

Proceedings of ICPRAM 2022, ISBN: 978-989-758-549-4

Quick Abstract

The Advancements in micro expression recognition techniques are accelerating at an exceptional rate in recent years. Envisaging a real environment, the recordings captured in our everyday life are prime sources for many studies, but these data often suffer from poor quality. Consequently, this has opened up a new research direction involving low resolution micro expression images. Identifying a particular class of micro expression among several classes is extremely challenging due to less distinct inter-class discriminative features. Low resolution of such images further diminishes the discriminative power of micro facial features. Undoubtedly, this increases the recognition challenge by twofold. To address the issue of low-resolution for facial micro expression, this work proposes a novel approach that employs a super resolution technique using Generative Adversarial Network and its variant. Additionally, Local Binary Pattern & Local phase quantization on three orthogonal planes are used for extracting facial micro features. The overall performance is evaluated based on recognition accuracy obtained using a support vector machine. Also, image quality metrics are used for evaluating reconstruction performance. Low resolution images simulated from the SMIC-HS dataset are used for testing the proposed approach and experimental results demonstrate its usefulness.

Fast Human Activity Recognition

Topics: Video Surveillance and Event Detection; Motion Detection; Action Recognition; Deep Learning and Neural Networks; Classification and Clustering; Image Analysis and Scene Understanding; Video Understanding; Video Indexing and Annotation; High-speed Computer Vision; Crowds Analysis

Authors: Shane Reid ¹ , Sonya Coleman ¹ , Dermot Kerr ¹ , Philip Vance ¹ and Siobhan O’Neill ²

Affiliations: ¹ School of Computing, Engineering and Intelligent Systems, Ulster University Magee Campus, Derry/Londonderry, Northern Ireland , ² School of Psychology, Ulster University Coleraine Campus, Coleraine, Northern Ireland

Digital Object Identifier: 10.5220/0010420300910098

Proceedings of IMPROVE 2021, ISBN: 978-989-758-511-1

Quick Abstract

Human activity recognition has been an open problem in computer vision for almost two decades. In that time there have been many approaches proposed to solve this problem, but very few have managed to solve it in a way that is sufficiently computationally efficient for real time applications. Recently this has changed, with keypoint based methods demonstrating a high degree of accuracy with low computational cost. These approaches take a given image and return a set of joint locations for each individual within an image. In order to achieve real time performance, a sparse representation of these features over a given time frame is required for classification. Previous methods have achieved this by using a reduced number of keypoints, but this approach gives a less robust representation of the individual’s body pose and may limit the types of activity that can be detected. We present a novel method for reducing the size of the feature set, by calculating the Euclidian distance and the direction of keypoint changes across a number of frames. This allows for a meaningful representation of the individuals movements over time. We show that this method achieves accuracy on par with current state of the art methods, while demonstrating real time performance.

Augmenting Guideline-based CDSS with Experts’ Knowledge

Topics: Knowledge Management; Decision Support Systems; Practice-based Research Methods for Healthcare IT

Authors: Nekane Larburu ¹ , Naiara Muro ¹ , Iván Macía ¹ , Eider Sánchez ² , Hui Wang ³ , John Winder ³ , Jacques Boaud ⁴ and Brigitte Séroussi ⁵

Affiliations: ¹ Vicomtech-IK4 and Biodonostia, Spain , ² NARU, Spain , ³ Ulster University, United Kingdom , ⁴ AP-HP, DRCD, Sorbonne Universités, UPMC Univ. Paris 06, INSERM and Université Paris 13, France , ⁵ Sorbonne Universités, UPMC Univ. Paris 06, INSERM, Université Paris 13, AP-HP, Hôpital Tenon and DSP, France

Digital Object Identifier: 10.5220/0006213903700376

Proceedings of HEALTHINF 2017/BIOSTEC, ISBN: 978-989-758-213-4

Quick Abstract

Over the past years, clinical guidelines have increasingly become part of the clinical daily practice in order to provide best available Evidence-Based-Medicine services. Hence, their formalization as computer interpretable guidelines (CIG) and their implementation in clinical decision support systems (CDSSs) are emerging to support clinicians in their decision making process and potentially improve medical outcomes. However, guideline compliancy in the clinical daily practice is still “low”. Some of the reasons for such low compliance rate are (i) lack of a complete guideline to cover special clinical cases (e.g. oncogeriatric cases), (ii) absence of parameters that current guidelines do not consider (e.g. lifestyle) and (iii) absence of up-to-date guidelines due to lengthy validation procedures. In this paper we present a novel method to build a CDSS that, besides integrating CIGs, stores experts’ knowledge to enrich the CDSS and provide best support to clinicians. The knowledge includes new evidence collected over time by the systematic usage of CDSSs.

Related Ontology Subjects/Areas/Topics (19)

Artificial Intelligence	Decision Support Systems, Remote Data Analysis	Knowledge-Based Systems
Biomedical Engineering	Enterprise Information Systems	Ontologies and the Semantic Web
Business Analytics	Health Engineering and Technology Applications	Practice-based Research Methods for Healthcare IT
Cardiovascular Technologies	Health Information Systems	Society, e-Business and e-Government
Computing and Telecommunications in Cardiology	Information Systems Analysis and Specification	Symbolic Systems
Data Engineering	Knowledge Management	Web Information Systems and Technologies
Decision Support Systems

Si elegans: A Computational Model of C. elegans Muscle Response to Light

Authors: Alicia Costalago Meruelo , Pedro Machado , Kofi Appiah and T. Martin McGinnity

Affiliation: Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005712201210126

Proceedings of NeBICA 2015/NEUROTECHNIX, ISBN: 978-989-758-161-8

Quick Abstract

It has long been the goal of computational neuroscientists to understand animal nervous systems, but their vast complexity has made it very difficult to fully understand even basic functions such as movement. The C. elegans nematode offers the opportunity to study a fully described connectome and link neural network to behaviour. In this paper a model of the responses of the body wall muscle in C. elegans to a random light stimulus is presented. An algorithm has been developed that tracks synapses in the nematode nervous system from the stimulus in the phototaxis sensory neurons to the muscles cells. A linear second order model was used to calculate the isometric force in each of the C. elegans body wall muscle cells. The isometric force calculated resembles that of previous investigations in muscle modelling.

Exploring Neural Principles with Si elegans, a Neuromimetic Representation of the Nematode Caenorhabditis elegans

Authors: Axel Blau ¹ , Frank Callaly ² , Seamus Cawley ² , Aedan Coffey ² , Alessandro de Mauro ³ , Gorka Epelde ³ , Lorenzo Ferrara ¹ , Finn Krewer ² , Carlo Liberale ¹ , Pedro Machado ⁴ , Gregory Maclair ³ , Thomas Martin McGinnity ⁴ , Fearghal Morgan ² , Andoni Mujika ³ , Alexey Petrushin ¹ , Gautier Robin ³ and John Wade ⁴

Affiliations: ¹ Fondazione Istituto Italiano di Tecnologia (IIT), Italy , ² National University of Ireland, Ireland , ³ Vicomtech-IK4, Spain , ⁴ University of Ulster and Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005190701890194

Proceedings of NeBICA 2014/NEUROTECHNIX, ISBN: 978-989-758-056-7

Quick Abstract

Biological neural systems are powerful, robust and highly adaptive computational entities that outperform conventional computers in almost all aspects of sensory-motor integration. Despite dramatic progress in information technology, there is a big performance discrepancy between artificial computational systems and brains in seemingly simple orientation and navigation tasks. In fact, no system exists that can faithfully reproduce the rich behavioural repertoire of the tiny worm Caenorhabditis elegans which features one of the simplest nervous systems in nature made of 302 neurons and about 8000 connections. The Si elegans project aims at providing this missing link. This article is sketching out the main platform components.

Modelling and Analysis of Retinal Ganglion Cells Through System Identification

Topics: Computational Neuroscience; Neuroinformatics and Bioinformatics; Bio-Inspired and Humanoid Robotics; Image Processing and Artificial Vision Applications; Intelligent Artificial Perception and Neural Sensors

Authors: Dermot Kerr ¹ , Martin McGinnity ² and Sonya Coleman ¹

Affiliations: ¹ University of Ulster, United Kingdom , ² Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005069701580164

Proceedings of NCTA 2014/IJCCI, ISBN: 978-989-758-054-3

Quick Abstract

Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Human-Computer Interaction	Neurotechnology, Electronics and Informatics
Bio-Inspired and Humanoid Robotics	Image Processing and Artificial Vision Applications	Pattern Recognition
Biomedical Engineering	Intelligent Artificial Perception and Neural Sensors	Physiological Computing Systems
Biomedical Signal Processing	Methodologies and Methods	Sensor Networks
Computational Intelligence	Neural Networks	Signal Processing
Computational Neuroscience	Neurocomputing	Soft Computing
Health Engineering and Technology Applications	Neuroinformatics and Bioinformatics	Theory and Methods

Si elegans - Computational Modelling of C. elegans Nematode Nervous System using FPGAs

Authors: Pedro Machado ¹ , John Wade ¹ and T. M. Mcginnity ²

Affiliations: ¹ University of Ulster, Ireland , ² University of Ulster and Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005169301690176

Proceedings of NeBICA 2014/NEUROTECHNIX, ISBN: 978-989-758-056-7

Quick Abstract

It has long been the goal of computational neuroscientists to understand and harness the parallel computational power of the mammalian nervous system. However, the vast complexity of such a nervous system has made it very difficult to fully understand even the most basic of functions such as movement and learning and accordingly there has been increasing attention paid to the development of emulations of simpler systems. One such system is the C. elegans nematode, which has been widely studied in recent years and there now exists a vast wealth of biological knowledge about its nervous structure, function and connectivity. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system behaviour to enable neuroscientists to better understand these basic functions. To fully replicate the C. elegans biological system requires powerful computing technologies, based on parallel processing, for real-time computation and therefore will use Field Programmable Gate Arrays (FPGAs) to achieve this. In this paper an overview of the complete hardware system required to fully realise Si elegans is presented along with an early small scale implementation of the hardware system.

A Proposed Framework for Supporting Behaviour Change by Translating Personalised Activities into Measurable Benefits

Topics: Social Networks; Health Care/Well-Being

Authors: Maurice Mulvenna ¹ , Adrian McCann ¹ , Maurice O'Kane ² , Barry Henderson ² , Karen Kirby ¹ and Deirdre McCay ³

Affiliations: ¹ University of Ulster, United Kingdom , ² Western Health and Social Care Trust and Altnagelvin Hospital, United Kingdom , ³ Altnagelvin Hospital, United Kingdom

Digital Object Identifier: 10.5220/0004505803060311

Proceedings of ICE-B 2013/ICETE, ISBN: 978-989-8565-72-3

Quick Abstract

The aim of this position paper is to examine the case for supporting behaviour change in pre-diabetic obese people in order to improve their health. The paper sets out the background and motivation for supporting behaviour change before outlining the relevant literature in this health and wellbeing area. The paper then explores the feasibility of SmartLife - a patient-driven application involving healthcare practitioners and peer support interaction with a focus on failure-free, positive reinforcement, patient empowerment and wellbeing.

Related Ontology Subjects/Areas/Topics (10)

Applications	e-Business	Platforms and Applications
Cloud Computing	Enterprise Information Systems	Social Networks
Collaboration and e-Services	Health Care/Well-Being	Telecommunications
Data Communication Networking

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

Topics: Learning Paradigms and Algorithms; Higher Level Artificial Neural Network Based Intelligent Systems; Self-Organization and Emergence

Authors: Anjan Kumar Ray , Gang Leng , T. M. Mcginnity , Sonya Coleman and Liam Maguire

Affiliation: University of Ulster, United Kingdom

Digital Object Identifier: 10.5220/0004555405070514

Proceedings of NCTA 2013/IJCCI, ISBN: 978-989-8565-77-8

Quick Abstract

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method.

Related Ontology Subjects/Areas/Topics (19)

Artificial Intelligence	Learning Paradigms and Algorithms	Physiological Computing Systems
Biomedical Engineering	Methodologies and Methods	Self-Organization and Emergence
Biomedical Signal Processing	Neural Networks	Sensor Networks
Computational Intelligence	Neurocomputing	Signal Processing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Soft Computing
Higher Level Artificial Neural Network Based Intelligent Systems	Pattern Recognition	Theory and Methods
Human-Computer Interaction

Strategic use of Twitter in Local Government - A Northern Ireland Study

Topics: Social Networks; Urban/Regional Planning/Development; Business Policy, Strategy and Governance; Internationalization and Social, Ethical and Cultural Factors; Technologies and Tools

Authors: Tiago Picão ¹ , Fiona McMahon ² , Valerie Purchase ² and Maurice Mulvenna ²

Affiliations: ¹ Universidade de Aveiro, Portugal , ² University of Ulster, United Kingdom

Digital Object Identifier: 10.5220/0004499002490255

Proceedings of ICE-B 2013/ICETE, ISBN: 978-989-8565-72-3

Quick Abstract

This paper presents the results of a survey of Twitter usage in Northern Ireland’s twenty-six councils. The data was gathered in Summer 2012. The research questions were developed from a review of the literature on use of social media by government and focused on the role of social media as a communication channel to local government, examining the dialogue between government and citizen and the sentiment of such dialogue. The results show significant heterogeneity in Twitter use amongst the councils; with many not engaging at all, while a small number were highly engaged with their citizens. Regardless of the perspectives of the councils, there was evidence that there was a demand from the citizens for conversations that was not being met by the councils. The paper recommends that councils need to define a social media strategy in order to maximise the use of social media, but reflects that the councils should find it easy to engage with citizens by simply asking them via Twitter.

Related Ontology Subjects/Areas/Topics (15)

Applications	e-Business	Social Networks
Business Policy, Strategy and Governance	Enterprise Information Systems	Sustainable e-Business
Cloud Computing	Internationalization and Social, Ethical and Cultural Factors	Technologies and Tools
Collaboration and e-Services	Logistics	Telecommunications
Data Communication Networking	Platforms and Applications	Urban/Regional Planning/Development

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