Artificial Intelligence Based Procedural Content Generation in Serious Games for Health: The Case of Childhood Obesity

Abstract

This paper presents a novel Procedural Content Generation (PCG) method aiming at achieving personalization and adaptation in serious games (SG) for health. The PCG method is based on a genetic algorithm (GA) and provides individualized content in the form of tailored messages and SG missions, taking into consideration data collected from health-related sensors and user interaction with the SG. The PCG method has been integrated into the ENDORSE platform, which harnesses the power of artificial intelligence (AI), m-health and gamification mechanisms, towards implementing a multicomponent (diet, physical activity, educational, behavioral) intervention for the management of childhood obesity. Within the use of the ENDORSE platform, a pre-pilot study has been conducted, involving the recruitment of 20 obese children that interacted with the platform for a period of twelve weeks. The obtained results, provide a preliminary justification of PCG’s effectiveness in terms of generating individualized content with sufficient relevance and usefulness. Additionally, a statistically significant correlation has been revealed between the content provided by the proposed PCG technique and lifestyle-related sensing data, highlighting the potential of the PCG’s capabilities in identifying and addressing the needs of a specific user.