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Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Van Woensel W,Roy PC,Abidi SSR,Abidi SR

doi

10.1016/j.artmed.2020.101931

subject

Has Abstract

pub_date

2020-08-01 00:00:00

pages

101931

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(20)30127-5

journal_volume

108

pub_type

杂志文章

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