# Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines
As we can notice, health professionals need to interpret the recommendation statement and adopt it to the individual's needs with his/her constraints, including physical activities preferences, contextual factors (e.g., environmental factors and personal factors), impairments from the body functions and structure, physical activity preferences, and any disorders or diseases [5]. In addition, compliance should be monitored, evaluated, and adapted to the individual's level of adherence and performance, on a regular basis. These are time-consuming activities, especially when aiming to monitor and support individuals at a worldwide scale. Consequently, these guidelines are often not applied, as they require significant time commitment by health professionals [6] and can also be too complex for elderly to follow without assistance [7; 8; 9]. We believe that a unified and coherent model for representing and reasoning about these recommendations can open the way to an effective integration of guideline management solutions with broader health applications (e.g., digital coach). To the best of our knowledge, to date, there is no computational model that can facilitate such an integration. Furthermore, the development of such a model is a challenging task, as it needs to deal with a number of issues, including: (i) establishing a shared and validated interpretation of the guidelines, considering the health professional perspective; (ii) quantifying, measuring, and combining different types of physical activities with different intensity, duration, etc; (iii) checking adherence to the recommendations over a given period of time; and (iv) re-evaluating and adapting recommendations to the user, whether or not there is adherence. The purpose of this paper is to address the above practical barriers and challenges by providing a reusable, operational, and machine-readable model that allows not only to represent computationally the guidelines but also to reason about them. To this end, we propose a symbolic AI approach that combines two main paradigms of knowledge representation and reasoning (KR&R): ontology and rules [10]. With respect to the first, we present HeLiFit, a domain ontology implemented in the Ontology Web Language (OWL), which models the key concepts and properties that characterize the notion of physical activity, as required by the WHO and ACSM. With respect to the second, we describe HeLiFit-Rule, a set of rules based on HeLiFit, implemented in the RDFox Rule language, to be used to express and reason about these guidelines in real-world health applications. In addition, to ensure a high level of syntactic and semantic interoperability, when integrating our solution with different systems, our framework is also compliant with the Fast Healthcare Interoperability Resources (FHIR) data model. As a result, such a model is configured as a solution that can be adopted as a plug-and-play module, when developing health-related systems, e.g., digital coach, which are intended to monitor users and and provide health recommendations to them [11]. Furthermore, another advantage of the proposed approach is that, being based on HeLiFit and HeLiFit-Rule, where one or more rules encode a particular element of the guidelines, is able to accommodate changes or extensions to the recommendations from WHO and/or ACSM in a modular way, with no need for an overall redesign or retraining of the model. In a nutshell, the key contributions of this work are the followings: (a) we identify real-world reference use cases and application scenarios motivating the needs of the proposedwork; (b) we introduce HeLiFit, a domain ontology implemented in OWL that provides a model, in term of concepts and properties, of the domain of interest; (c) we describe HeLiFit-Rule, a set of rules representing recommendations for physical activity, as per WHO/ACSM guidelines; (d) we describe an evaluation with domain experts, in terms of our system's ability to model user compliance and performance and adapt its advice accordingly.