William Van Woensel

@article{VANWOENSEL2020101931,
title = {Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods},
author = {William Van Woensel and Patrice C Roy and Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {http://www.sciencedirect.com/science/article/pii/S0933365720301275
https://authors.elsevier.com/a/1bTwR3KEGaD3xR},
doi = {https://doi.org/10.1016/j.artmed.2020.101931},
issn = {0933-3657},
year = {2020},
date = {2020-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {108},
pages = {101931},
abstract = {In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.},
keywords = {Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care},
pubstate = {published},
tppubtype = {article}
}

@article{5999639,
title = {Mobile Querying of Online Semantic Web Data for Context-Aware Applications},
author = { William Van Woensel and Sven Casteleyn and Elien Paret and Olga De Troyer},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile%20Querying%20of%20Online%20Semantic%20Web%20Data%20for%20Context-Aware%20Applications.pdf},
issn = {1089-7801},
year = {2011},
date = {2011-01-01},
journal = {IEEE Internet Computing Special Issue (Semantics in Location-Based Services)},
volume = {15},
number = {6},
pages = {32--39},
abstract = {Mobile devices are increasingly multifunctional and personal, providing mobile applications with the necessary user information to achieve personalization. At the same time, detection technologies let such devices find nearby physical entities and thus map the user\'s environment. By exploiting existing online Semantic Web sources about these detected entities, mobile applications can further improve personalization. SCOUT is a mobile application framework that supports linking physical entities to online semantic data sources. It provides applications with an integrated, query-able view on these sources and the user\'s environment. The authors developed a tailored data management approach to efficiently access these distributed online semantic sources.},
keywords = {Mobile Computing, Semantic Web},
pubstate = {published},
tppubtype = {article}
}

@article{Casteleyn:2009:AAS:1541709.1541712,
title = {Aspect-Oriented Adaptation Specification in Web Information Systems: A Semantics-based Approach},
author = { Sven Casteleyn and William Van Woensel and Kees van der Sluijs and Geert-Jan Houben},
url = {http://web.cs.dal.ca/~woensel/paper/Aspect-Oriented Adaptation Specification in Web Information Systems a Semantics-based Approach.pdf},
issn = {1361-4568},
year = {2009},
date = {2009-01-01},
journal = {New Review of Hypermedia and Multimedia},
volume = {15},
number = {1},
pages = {39--71},
publisher = {Taylor & Francis, Inc.},
address = {Bristol, PA, USA},
abstract = {By tailoring content access, presentation, and functionality to the user’s location, device, personal preferences and needs, Web Information Systems have become increasingly user- and context dependent. In order to realize such adaptive behavior, Web engineers are thus faced with an additional challenge: engineering the required adaptation concerns. In this article, we present, in the context of a Web Information System design method, an adaptation engineering process that is separated from the regular Web design process. Our approach is based on the use of two key elements: (i) aspect-oriented techniques to achieve the separation of (adaptation) concerns, and (ii) the exploitation of semantic information and meta-data associated with the content, for enhanced expressivity and flexibility. By combining these key elements, we demonstrate a robust, rich, consistent and flexible way to specify adaptation in Web Information Systems.},
keywords = {Aspect-orientation, Semantic Web},
pubstate = {published},
tppubtype = {article}
}