# 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
#### 2.3.4 Conceptualization and Formalization
responsible, such as performing a physical activity; the patient is linked to the physical activity by means of the object property carried out by; (3) collect all the measurements characterized by dimensions as quantifiable properties of the physical activity (duration, frequency,...) and the patient (age, height,...) that can be approximated by values and have units; (4) model the recommendations that the patient is subject to, as a symbolic object where each recommendation has a unique _Appellation_ (WHOcode001, WHOcode002...). Based on these patterns, we have proceeded with their extension to model all the domain specific concepts and properties, including physical activity, exercise, cardio-aerobic, etc. and all the relevant measurements when performing and recording it, as required by the WHO and ACSCM guidelines. In Figures 5 and 6, we illustrate how the main the CIDOC-CRM-based patterns are extended to capture the underlying domain knowledge. In particular, Figure 5 shows the extension of the activity (E7 Activity) to model the _Actor-Activity-Performance-Pattern_ that schematizes when a person performs one or more physical activities and, for each of them, obtains a set of performance parameters--e.g., duration, step counts, VO2 Max, and so on. These are modeled as subclasses of _E54 Dimension_, which comprises quantifiable properties that can be measured by some calibrated means and approximated by numerical values. Likewise, Figure 6 shows the  
Figure 5: Part of classes of HeliFit-_Actor-Activity-Performance-Pattern_. Figure 6: Part of classes of HeliFit-Actor-Anthropometric-Vital-Signs-Measurements. extension of the the measurements (E16 Measurement) to be able to model the _Actor-Anthropometry-Vital-Signs-Measurements-Pattern_ that schematizes when a person performs either an Anthropometric or a vital signs measurements. It is relevant to highlight here that HeLiFit is not supposed to be a single ontology covering the entirety of what exists w.r.t. physical activity. Instead, it aims to formalize a domain model that provides suitable abstractions of the domains under consideration, to cover the use cases of frailty, mental health, and physical activity, and to enable computational support for issuing recommendations, in accordance with the guidelines published by WHO and ACSM. HeLiFit-Rules.HeliFit-Rules are the set of rules used for formalizing and triggering recommendations. These were implemented using the RDFox Rule Language--a declarative logic-based language of type IF-THEN, which is part of the RDFox engine for high-performance knowledge graph and semantic reasoning [50]. In this section, we will describe the HeliFit-Rules in detail by first introducing a set of rules from the WHO/ACSM guidelines and then presenting the relevant rule representation in terms of the RDFox Rule Language. Table 2 presents a subset of WHO recommendations for the four categories, namely children and adolescents, adults (aged 18-64 years), older adults (aged 65 years and older), and pregnant and postpartum women. Next, we discuss HeLiFit-Rule, the implementation of WHO and ACSM recommendations, which relies on the HeLiFit ontology and is based on the Rule paradigm [10]. One of the first challenges was to address the comparison of the performances of different physical activities and issue a strategy to specify a specific physical activity that all the others can be reduced to. In other words, we want to be able to state that performing physical activity X1 for a duration Y1 and intensity Z1 is equivalent to performing physical activity X2 for a duration Y2 and intensity Z2. We do this by reducing all physical activities to walking and to a number of steps [51]:  
1. **Rule to convert a physical activity into steps:** In order to make physical activities comparable, and hence apply aggregation operations when more that one physical activity is performed over a specific time window, we convert them into steps using the duration and an activity-specific conversion factor, as shown in Equation (1).