Browsing by Author "Moore, G."

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    Assessment and Visualization of Parkinson's Disease Tremor
    (IEEE, 2011-01-17) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    Current clinical methods of Parkinson's disease assessment are known to be subjective and potentially error prone. This paper presents a preliminary investigation into the development of a novel approach for the assessment of Parkinsonian tremor. The approach is based on a computer vision technique with the aim of providing a more objective, frequent, yet unobtrusive means of assessment. A method of tremor amplitude quantification is proposed, and 3D visualization techniques are exploited to provide an intuitive tool for the monitoring and assessment of Parkinson's disease. The approach developed has the potential to address the limitations and patient privacy concerns associated with video-based monitoring technology. Initial results following evaluation have demonstrated the viability and benefits of the approach, including objectiveness, repeatability and clarity of activity performance results.
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    The creation of simulated activity datasets using a graphical intelligent environment simulation tool
    (IEEE, 2014-11-06) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    The availability of datasets capturing the performance of activities of daily living is limited by difficulties associated with the collection of such data. Software solutions can mitigate these limitations, providing researchers with the ability to rapidly generate simulated data. This paper describes the use of IE Sim to create a simulated intelligent environment within which activities of daily living can be performed using a virtual avatar. IE Sim has been demonstrated to facilitate the generation of datasets capturing normal activity performance in addition to overlapping activities and abnormal activities such as hazardous scenarios.
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    Flexible and Customizable Visualization of Data Generated within Intelligent Environments
    (IEEE, 2012-09) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    This paper outlines a tool for the visualization of data generated within Intelligent Environments. This tool has been designed with a focus on flexibility and customizability hence facilitating application to a range of areas including institutionalized or home-based healthcare monitoring. Through the use of an object toolbox, non-technical users can rapidly re-create a visual representation (aka a "Scene") of an intelligent environment and connect this scene to an active data repository. Data generated within the environment can be visualized in real-time, or summarized using a density ring visualization format that can be customized based on user defined rules to highlight events of particular interest. The tool was tested within a smart lab used as an active research environment. Collection of data over a one week period resulted in 3840 sensor activations. Visualization of this dataset illustrates the potential of the tool to highlight normal and abnormal activity trends within the environment.
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    IE Sim - A Flexible Tool for the Simulation of Data Generated within Intelligent Environments
    (Springer, 2012-11) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    Availability of datasets generated by Intelligent Environments for the testing of new approaches may be limited due to constraints including time, space, and money. The use of simulated Intelligent Environments offers a method of creating datasets with maximum control and minimal costs and constraints. Such datasets facilitate the testing of novel approaches to areas such as activity recognition and ambient assisted living. IE Sim is a flexible feature-rich approach that supports graphical interactive construction and simulation of virtual Intelligent Environments This paper discusses the key features of IE Sim and discusses the results of a software evaluation performed by 21 international researchers with an interest in such datasets. Results from the evaluation rated IE Sim highly in terms of ease of use and usefulness in research and identified key requirements for future developments.
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    Remote Monitoring and Visualisation of Parkinson's Disease Tremor
    (Northern Ireland Biomedical Engineering Society, 2010-08-25) Moore, G.; Nugent, Chris; Chen, Liming; Synnott, J.
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    WiiPD - An Approach for the Objective Home Assessment of Parkinson's Disease
    (IEEE, 2011-08-30) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    Activity modelling and discovery plays a critical role in smart home based assisted living. Existing approaches to pattern recognition using data-intensive analysis suffers from various drawbacks. To address these shortcomings, this paper introduces a novel ontology-based approach to activity modelling, activity discovery and evolution. In this approach, activity modelling is undertaken through ontological engineering by leveraging domain knowledge and heuristics. The generated activity models evolve from the initial “seed” activity models through continuous activity discovery and learning. Activity discovery is performed through ontological reasoning. The paper describes the approach in the context of smart home with special emphases placed on activity discovery algorithms and evolution mechanism. The approach has been implemented in a feature-rich assistive living system in which new daily activities can be detected and further used to evolve the underlying activity models.
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    WiiPD: Objective Home Assessment of Parkinson's Disease using the Nintendo Wii Remote
    (IEEE, 2012-08-28) Synnott, J.; Chen, Liming; Nugent, Chris; Moore, G.
    Current clinical methods for the assessment of Parkinson's disease (PD) suffer from inconvenience, infrequency, and subjectivity. WiiPD is an approach for the objective home-based assessment of PD which utilizes the intuitive and sensor-rich Nintendo Wii remote. Combined with an electronic patient diary, a suite of minigames, a metric analyzer, and a visualization engine, we propose that this system can complement existing clinical practice by providing objective metrics gathered frequently over extended periods of time. In this paper, we detail the approach and introduce a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with PD. The system has been tested on a 71-year-old participant with PD over a period of 15 days, a 72-year-old control user without PD, and a group of eight young adults. Results indicate a clear correlation between patient self-rating scores of tremor severity and metric values obtained, in addition to clear differences in metrics obtained from each user group. These results suggest that this approach is capable of indicating the presence and severity of the motor symptoms of PD that affect arm motor control.