Browsing by Author "O'Connor, S."

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    Assessing the Perceived Realism of Agent Grouping Dynamics for Adaptation and Simulation
    (Elsevier, 2019-10-14) O'Connor, S.; Shuttleworth, J.; Colreavy-Donnelly, S.; Liarokapis, F.
    Virtual crowds are a prominent feature for a range of applications; from simulations for cultural heritage, to interactive elements in video games. A body of existing research seeks to develop and improve algorithms for crowd simulation, typically with a goal of achieving more realistic behaviours. For applications targeting human interaction however, what is judged as realistic crowd behaviour can be subjective, leading to situations where actual crowd data is not always perceived to be more real than simulation, making it difficult to identify a ground truth. We present a novel method using psychophysics to assess the perceived realism of behavioural features with respect to virtual crowds. In this instance, a focus is given to the grouping dynamics feature, whereby crowd composition in terms of group frequency and density is evaluated through thirty-six conditions based on crowd data captured from three pedestrianised real-world locations. The study, conducted with seventy-eight healthy participants, allowed for the calculation of perceptual thresholds, with configurations identified that appear most real to human viewers. The majority of these configurations correlate with the values extracted from the crowd data, with results suggesting that viewers have more perceptual flexibility when group frequency and density are increased, rather than decreased.
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    Developing Gamified Elements to Influence Positive Behavioural Change towards Organisational Energy Efficiency
    (Academic Conferences International Limited, 2017-10-05) O'Connor, S.; Doukianou, S.; Awad, M.; Dixon, R.; O’Neill, D.; Dunwell, I.
    Demands for energy within public sector buildings, such as administrative offices, cultural heritage sites, and museums, represent a significant financial and environmental burden. With issues relating to climate change now more prominent than ever, energy efficiency is an important aspect for consideration at both organisational and occupant levels in public buildings. Occupant behaviour plays a key role in the process of saving energy, with the major areas of wastage being directly linked to the use of heating, lighting and electrical devices. Automating these devices can provide a partial, if costly, solution; however, the influence of personal preferences on comfort levels and productivity must also be considered. Strategies may thus seek to enhance organisational energy efficiency in the public sector by promoting positive behavioural changes amongst occupants or visitors. However, such strategies must be informed by knowledge of related behaviours, business processes and best practices for saving energy within specific workplace contexts. To encourage and support participants in adopting energy-conscious behaviours, the incorporation of serious games and gamification offers potential to bring about positive behavioural change. This paper presents the OrbEEt Behavioural Change Framework and its application through the development of a gamified ecosystem consisting of three interfaces; a smartphone game, an intranet portal, and an in-office display. This involves the incorporation of behavioural triggers through an infrastructure of high granularity sensor data, the identification of which are informed by the results of a questionnaire targeting 28 participants across four European pilot sites, representing a diverse range of cultural, climatic, and operational settings for public sector buildings. The work herein represents the pre-intervention stage of the ongoing 3-year OrbEEt research project, with the potential application of these behavioural triggers and interfaces extending to various organisations that are looking to improve overall energy efficiency, while maintaining business productivity and ensuring best practices.
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    EmotiMask: Mapping Mouth Movements to an LED Matrix for Improving Recognition When Teaching With a Face Mask
    (The International Academic Forum (IAFOR), 2022-09-27) Hasshu, S.; O'Connor, S.; Colreavy-Donnelly, S.; Kuhn, S.; Caraffini, F.
    The Covid-19 pandemic has led to the adoption of face masks in physical teaching spaces across the world. This has in-turn presented a number of challenges for practitioners in the face-to-face delivery of content and in effectively engaging learners in practical settings, where face coverings are an ongoing requirement. Being unable to identify the mouth movements of a speaker due to the lower portion of the face being obscured can lead to issues in clarity, attention, emotional recognition, and trust attribution, negatively affecting the learning experience. This is further exacerbated for those who require specialist support and those with impairments, particularly those centred around hearing. EmotiMask embeds an LED matrix within a face mask to replicate mouth movements and emotional state through speech detection and intelligent processing. By cycling through different LED configurations, the matrix can approximate speech in-progress, as well as various mouth patterns linked to distinct emotional states. An initial study placed EmotiMask within a HE practical session containing 10 students, with results suggesting a positive effect on clarity and emotional recognition over typical face masks. Further feedback noted that it was easier to identify the current speaker with EmotiMask, however speech amplification, additional led configurations, and improved portability are desired points of refinement. This study represents a step towards a ubiquitous solution for tackling some of the challenges presented when teaching in a pandemic or similar situations where face coverings are a requirement and has potential value in other sectors where such scenarios present themselves.
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    Fostering Engagement with Cultural Heritage Through Immersive VR and Gamification
    (Springer, Cham, 2020-04-08) O'Connor, S.; Colreavy-Donnelly, S; Dunwell, I
    Digital games provide a recognised means of engagement and education when addressing challenges in educating and immersing individuals in their own heritages, and those of other cultures. Similarly, gamification techniques, commonly expressed as the addition of game elements to an existing process, have been successfully applied to augment existing resources and programmes. The many examples of gamification or serious games focusing on cultural heritage also highlight the potential benefits of using these principles for the purposes of supporting preservation and learning. In this chapter, we present I-Ulysses, a virtual-reality game designed to engage based around the notable work Ulysses by Irish author James Joyce. The rationale for the selection of Ulysses as a basis for the game’s content and design was two-fold; firstly because of its cultural impact within Ireland, and secondly as its content appeared well-suited to exploration as a virtual reality experience. Facets of gamification are explored in I-Ulysses through key mechanics, including a focus towards virtual worlds and crowd intelligence based on real-world data, to highlight how these principles can be employed for cultural heritage preservation and knowledge transfer. Through feedback obtained from focus groups interacting with I-Ulysses, it can be seen that the gamified mechanics presented through the lens of virtual reality provide an informative and educational guide to Ulysses that would engage and appeal to a wide audience.
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    I-Ulysses: A Technical Report
    (Elsevier, 2019-10-31) Colreavy-Donnelly, S.; O'Connor, S.; Homapour, E.
    The I-Ulysses: Poetry in Motion project is as a virtual reality experience, guiding the user through the unfolding events of James Joyce's Ulysses in real-time. Currently there is a lack of research looking at adapting literature into virtual reality, for the purposes of cultural heritage, or for serious learning. I-Ulysses is aimed at addressing this gap, by providing an educational tool, intended to help the user understand key aspects of the book. What follows is a technical report, outlining the objectives, aims and results of the I-Ulysses project.
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    Identifying Parkinson’s Disease Through the Classification of Audio Recording Data
    (IEEE, 2020-07) Bielby, James; Kuhn, Stefan; Colreavy-Donnelly, S.; Caraffini, Fabio; O'Connor, S.; Anastassi, Zacharias
    Developments in artificial intelligence can be leveraged to support the diagnosis of degenerative disorders, such as epilepsy and Parkinson’s disease. This study aims to provide a software solution, focused initially towards Parkinson’s disease, which can positively impact medical practice surrounding degenerative diagnoses. Through the use of a dataset containing numerical data representing acoustic features extracted from an audio recording of an individual, it is determined if a neural approach can provide an improvement over previous results in the area. This is achieved through the implementation of a feedforward neural network and a layer recurrent neural network. By comparison with the state-of-the-art, a Bayesian approach providing a classification accuracy benchmark of 87.1%, it is found that the implemented neural networks are capable of average accuracy of 96%, highlighting improved accuracy for the classification process. The solution is capable of supporting the diagnosis of Parkinson’s disease in an advisory capacity and is envisioned to inform the process of referral through general practice.
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    An initial study to assess the perceived realism of agent crowd behaviour in a virtual city
    (IEEE, 2013-09-11) O'Connor, S.; Liarokapis, F.; Peters, C.
    This paper examines the development of a crowd simulation in a virtual city, and a perceptual experiment to identify features of behaviour which can be linked to perceived realism. This research is expected to feedback into the development processes of simulating inhabited locations, by identifying the key features which need to be implemented to achieve more perceptually realistic crowd behaviour. The perceptual experimentation methodologies presented can be adapted and potentially utilised to test other types of crowd simulation, for application within computer games or more specific simulations such as for urban planning or health and safety purposes.
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    A Neural Network for Interpolating Light-Sources
    (IEEE, 2020-07-13) Colreavy-Donnelly, S.; Kuhn, Stefan; Caraffini, Fabio; O'Connor, S.; Anastassi, Zacharias; Coupland, Simon
    This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method interpolates light-sources by utilising a line-algorithm, which detects the direction of light sources in the image. Both of these methods are single-image solutions and employ deterministic methods to calculate the values from the image alone, without the need for illumination-models. They extract real-time geometry from the light source in an image, rather than mapping an illumination-model onto the image, which are the only models used today. Finally, those outputs are used to train a Convolutional Neural Network. This displays greater accuracy than previous methods for shadow detection and can predict light source-direction and thus orientation accurately, which is a considerable innovation for an unsupervised CNN. It is significantly faster than the deterministic methods. We also present a reference dataset for the problem of shadow and light direction detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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    Perceived Realism of Crowd Behaviour with Social Forces
    (IEEE, 2015-07-22) O'Connor, S.; Liarokapis, F.; Jayne, C.
    This paper investigates the development of an urban crowd simulation for the purposes of psychophysical experimentation. Whilst artificial intelligence (AI) is advancing to produce more concise and interesting crowd behaviours, the number or sophistication of the algorithms implemented within a system does not necessarily guarantee its perceptual realism. Human perception is highly subjective and does not always conform to the reality of the situation. Therefore it is important to consider this aspect when dealing with AI implementations within a crowd system aimed at humans. In this research an initial two alternative forced choice (2AFC) with constant stimuli psychophysical experiment is presented. The purpose of the experiment is to assess whether human participants perceive crowd behaviour with a social forces model to be more realistic. Results from the experiment suggest that participants do consider crowd behaviour with social forces to be more realistic. This research could inform the development of crowd-based systems, especially those that consider viewer perception to be important, such as for example video games and other media.
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    A perceptual study into the behaviour of autonomous agents within a virtual urban environment
    (IEEE, 2013-06-04) O'Connor, S.; Liarokapis, F.; Peters, C.
    Simulating vast crowds of autonomous agents within a procedurally generated virtual environment is a challenging endeavour from a technical perspective, however it becomes even more difficult when the subjective nature of perception is also taken into account. Agent behaviour is the product of artificial intelligence systems working in tandem, however the sophistication of these systems is not a guarantee of achieving believable behaviour. Within locations based upon reality such as an urban environment, the perceived realism of agent behaviour becomes even harder to achieve. This paper presents the development of a crowd simulation that is based upon a real-life urban environment, which is then subjected to perceptual experimentation to identify features of behaviour which can be linked to perceived realism. This research is predicted to feedback into the development processes of inhabited cities, especially those attempting to simulate perceptually realistic agents as it will highlight features of behaviour that are important to implement. The perceptual experimentation methodologies presented can also be adapted and potentially utilised to test other types of crowd simulation, whether it be for the purposes of computer games or even urban planning and health and safety.