J
Jonathan Synnott
Researcher at Ulster University
Publications - 46
Citations - 443
Jonathan Synnott is an academic researcher from Ulster University. The author has contributed to research in topics: Activity recognition & Smart environment. The author has an hindex of 11, co-authored 46 publications receiving 335 citations.
Papers
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Journal ArticleDOI
Simulation of Smart Home Activity Datasets.
TL;DR: This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars.
Proceedings ArticleDOI
The creation of simulated activity datasets using a graphical intelligent environment simulation tool
TL;DR: The use of IE Sim is described to create a simulated intelligent environment within which activities of daily living can be performed using a virtual avatar to facilitate the generation of datasets capturing normal activity performance in addition to overlapping activities and abnormal activities such as hazardous scenarios.
Journal ArticleDOI
WiiPD—Objective Home Assessment of Parkinson's Disease Using the Nintendo Wii Remote
TL;DR: WiiPD is an approach for the objective home-based assessment of PD which utilizes the intuitive and sensor-rich Nintendo Wii remote and a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with PD.
Journal ArticleDOI
A Scalable, Research Oriented, Generic, Sensor Data Platform
Joseph Rafferty,Jonathan Synnott,Chris D. Nugent,Andrew Ennis,Philip A. Catherwood,Ian McChesney,Ian Cleland,Sally McClean +7 more
TL;DR: SensorCentral provides a framework which enables interoperability with a large range of agnostic sensor devices whilst simultaneously providing features which support research, which will integrate this platform into the open data initiative enabling collaboration with the international community of researchers.
Book ChapterDOI
SensorCentral: A Research Oriented, Device Agnostic, Sensor Data Platform
TL;DR: This study introduces a research oriented, device agnostic sensor, data platform called SensorCentral which incorporates several research oriented features such as offering annotation interfaces, metric generation, exporting experimental datasets, machine learning services, rule based classification, forwarding live sensor records to other systems and quick sensor configuration.