The Library
Toward a symbolic AI approach to the WHO/ACSM physical activity sedentary behavior guidelines
Tools
Allocca, Carlo, Jilali, Samia, Ail, Rohit, Lee, Jaehun, Kim, Byungho, Antonini, Alessio, Motta, Enrico, Schellong, Julia, Stieler, Lisa, Haleem, Muhammad Salman, Georga, Eleni, Pecchia, Leandro, Gaeta, Eugenio and Fico, Giuseppe (2022) Toward a symbolic AI approach to the WHO/ACSM physical activity sedentary behavior guidelines. Applied Sciences, 12 (4). e1776. doi:10.3390/app12041776 ISSN 2076-3417.
|
PDF
WRAP-toward-symbolic-AI-approach-WHO-ACSM-physical-activity-sedentary-behavior-guidelines-Pecchia-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2106Kb) | Preview |
Official URL: https://doi.org/10.3390/app12041776
Abstract
The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | R Medicine > RM Therapeutics. Pharmacology | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Exercise therapy, Physical fitness, Artificial intelligence, Health promotion -- Technological innovations , Exercise -- Health aspects, Sedentary behavior -- Health aspects | ||||||
Journal or Publication Title: | Applied Sciences | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2076-3417 | ||||||
Official Date: | 9 February 2022 | ||||||
Dates: |
|
||||||
Volume: | 12 | ||||||
Number: | 4 | ||||||
Article Number: | e1776 | ||||||
DOI: | 10.3390/app12041776 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 3 March 2022 | ||||||
Date of first compliant Open Access: | 7 March 2022 | ||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year