Designing an Indoor Real-Time Location System for Healthcare Facilities

Falleri, Noemi; Luschi, Alessio; Gusinu, Roberto; Terzaghi, Filippo; Iadanza, Ernesto

doi:10.1007/978-3-030-72805-2_8

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…It will integrate the ODIN Platform with an informative system able to empower the management of a large-scale pilot (hospital "Le Scotte" in Siena, Italy) in terms of clinical engineering, logistics, and disaster preparedness. OHIO will enhance the existing hospital's Computer Aided Facility Management (CAFM) system named SPOT [23]- [25], and an Indoor Positioning System (IPS) mobile application named HiWAY [26], [27], to improve the process of maintenance of medical equipment, streamline logistics, and support the top management in designing effective response to disasters. OHIO will be fully integrated within the ODIN Platform, as all the modules and data will be registered as KERs, thus extending the ODIN Ontology, and exploiting the offered services and features.…”

Section: B the Odin Project And Ohiomentioning

confidence: 99%

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

et al. 2023

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The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence.

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Section: B the Odin Project And Ohiomentioning

confidence: 99%

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

et al. 2023

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OHIO - Odin Hospital Indoor Compass for Empowering the Management of Hospitals

Luschi,

Iadanza

2024

IFMBE Proceedings

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RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

Onofri

Corbetta

2022

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Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural analyses and comfort/performance improvements, is seeing increasing diffusion. Conceptually, this approach enables room-level localisation based on a network of small antennas (thus, without invasive modification of the existent structures). The antennas measure the intensity (RSSi) of self-advertising signals broadcasted by beacons individually assigned to the visitors. The signal intensity provides a proxy for the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd, ...). In this contribution, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages >96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).

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Designing an Indoor Real-Time Location System for Healthcare Facilities

Cited by 3 publications

References 40 publications

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

OHIO - Odin Hospital Indoor Compass for Empowering the Management of Hospitals

RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

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