Patrice C. Roy

@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{Roy2017b,

title = {Possibilistic Activity Recognition with Uncertain Observations to Support Medication Adherence in an Assisted Ambient Living Setting},

author = {Patrice C. Roy and Samina R. Abidi and Syed S.R. Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S0950705117303246},

doi = {10.1016/j.knosys.2017.07.008},

issn = {09507051},

year  = {2017},

date = {2017-07-06},

journal = {Knowledge-Based Systems},

volume = {133},

pages = {156-173},

abstract = {A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources' types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.},

keywords = {Activity Recognition, Ambient Intelligence, medication adherence},

pubstate = {published},

tppubtype = {article}

}