Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines
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 recommen...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/1776 |
| _version_ | 1797483153472880640 |
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| author | Carlo Allocca Samia Jilali Rohit Ail Jaehun Lee Byungho Kim Alessio Antonini Enrico Motta Julia Schellong Lisa Stieler Muhammad Salman Haleem Eleni Georga Leandro Pecchia Eugenio Gaeta Giuseppe Fico |
| author_facet | Carlo Allocca Samia Jilali Rohit Ail Jaehun Lee Byungho Kim Alessio Antonini Enrico Motta Julia Schellong Lisa Stieler Muhammad Salman Haleem Eleni Georga Leandro Pecchia Eugenio Gaeta Giuseppe Fico |
| author_sort | Carlo Allocca |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T22:43:51Z |
| format | Article |
| id | doaj.art-c54b816d15e945b3a7990e10fdb803fb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:43:51Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c54b816d15e945b3a7990e10fdb803fb2023-11-23T18:33:57ZengMDPI AGApplied Sciences2076-34172022-02-01124177610.3390/app12041776Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior GuidelinesCarlo Allocca0Samia Jilali1Rohit Ail2Jaehun Lee3Byungho Kim4Alessio Antonini5Enrico Motta6Julia Schellong7Lisa Stieler8Muhammad Salman Haleem9Eleni Georga10Leandro Pecchia11Eugenio Gaeta12Giuseppe Fico13Health Innovation, Samsung, Communications House, South St., Staines TW18 4QE, UKHealth Innovation, Samsung, Communications House, South St., Staines TW18 4QE, UKHealth Innovation, Samsung, Communications House, South St., Staines TW18 4QE, UKSamsung Research, 56 Seongchon-gil, Seoul 06765, KoreaSamsung Research, 56 Seongchon-gil, Seoul 06765, KoreaKnowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UKKnowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UKDepartment of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, GermanyDepartment of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, GermanySchool of Engineering, University of Warwick, Library Rd, Coventry CV4 7AL, UKDepartment of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, GreeceSchool of Engineering, University of Warwick, Library Rd, Coventry CV4 7AL, UKLife Supporting Technologies (LifeSTech), ETSI Telecomunicaciones, Universidad Politécnica de Madrid, Av. Complutense, 30, 28040 Madrid, SpainLife Supporting Technologies (LifeSTech), ETSI Telecomunicaciones, Universidad Politécnica de Madrid, Av. Complutense, 30, 28040 Madrid, SpainThe 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.https://www.mdpi.com/2076-3417/12/4/1776symbolic AIontologyrulesWHO/ACSM physical activity guidelinesknowledge representation and reasoning |
| spellingShingle | Carlo Allocca Samia Jilali Rohit Ail Jaehun Lee Byungho Kim Alessio Antonini Enrico Motta Julia Schellong Lisa Stieler Muhammad Salman Haleem Eleni Georga Leandro Pecchia Eugenio Gaeta Giuseppe Fico Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines Applied Sciences symbolic AI ontology rules WHO/ACSM physical activity guidelines knowledge representation and reasoning |
| title | Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines |
| title_full | Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines |
| title_fullStr | Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines |
| title_full_unstemmed | Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines |
| title_short | Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines |
| title_sort | toward a symbolic ai approach to the who acsm physical activity sedentary behavior guidelines |
| topic | symbolic AI ontology rules WHO/ACSM physical activity guidelines knowledge representation and reasoning |
| url | https://www.mdpi.com/2076-3417/12/4/1776 |
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