# Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines
## 2 Materials and Methods
### Symbolic AI Approach: From Analysis to Design
#### 2.3.3 Domain and Top-Level Ontology Analysis
These include notions such as VO2 MAX, HRR, MET, and others, which are captured through personal sensor devices (wearables and/or smart phone) and are of primary importance when dealing with the high-resolution temporal data associated with physical activity. OPE ([[https://bioportal.bioontology.org/ontologies/OPE](https://bioportal.bioontology.org/ontologies/OPE)]([https://bioportal.bioontology.org/ontologies/OPE](https://bioportal.bioontology.org/ontologies/OPE)), accessed on 28 November 2021) (the Ontology of Physical Exercises) [47] provides a reference for describing an exercise in terms of functional movements, emphasizing the involvement of the _Musculoskeletal_ and _Muscle_ parts when performing a specific type of fitness: exergaming. Therefore, OPE has quite a few limitations in representing nongame-based physical activities with sufficient detail. PACO [24] (Physical Activity Ontology) supports the structuring and standardizing of heterogeneous descriptions of physical activities to address semantic interoperability. It has been built by extracting concepts related to physical activity from medical corpora, including questionnaires and assessment scales. PACO, with 225 classes, 20 object properties, 1 data property, and 23 instances, includes the notion of exercise leisure activity and a number of modifiers, such as _Amount_, _Frequency_, and _Intensity_. However, it does not provide a classification of the activities in aerobic and anaerobic ones, and does not provide classes and proprieties to measure and estimate the intensity of an activity (e.g., VO2 MAX, HRR, MET), which are needed for formalizing WHO rules. As far as HeLaIS (Healthy Lifestyle Support ) is concerned [25], it aims at modelling foods and nutrients as well as physical activities. However, its primary focus is on the specific foods individuals must consume, rather than the contextual physical activity elements that are needed to personalize the WHO recommendations. Nevertheless, all the above ontologies have inspired technical aspects of the engineering of the HeLiFit ontology, as per relevant overlapping objectives, e.g., the structure of physical activities and performance context. **Top-Level Ontologies:** (Refer to [48] for a full survey of Top-Level Ontologies) With the aim of connecting our work to a top-level ontology, we have examined the most relevant three: DOLCE [42], CIDOC-CRM [43], and SUMO [44]. The first one, DOLCE, is oriented toward capturing the ontological categories underlying natural language and human common sense, providing high level classes, such as _Event_. This encompasses at least one _Agent_ that _isParticipantIn_ it, and executes a _Task_, which typically _isDefinedIn_ a _Plan_, _Workflow_, or _Project_. The second one, CIDOC-CRM, focuses on representing event based cultural heritage and contains generic upper classes, such as _Space-Time_, _Events_, _Activity_, and _Measurement_. Finally, the third one, SUMO (Suggested Upper Merged Ontology), is a formal ontology and it is defined in the higher order logical language of SUO-KIF. It includes dozens of domains ontologies, contains roughly 20,000 terms and 80,000 logical statements and it is largely used for translations to languages and mappings to WordNet [44]. We suggest [49] for a deeper overview of existing foundational ontologies and how they are used across several computer-based tasks. In conclusion, although all these three ontologies could be in principle adopted as our top level ontology, we selected CIDOC-CRM, as it provides an ontology design pattern that can be easily extended for use in the context of modeling WHO/ACSM recommendations.