Prompting Large Language Models for Tailored Exercise Recommendations in Office Spaces

In the digital era, Recommender Systems are a crucial component, commonly used in services such as music and movie streaming. Despite their widespread adoption, surprisingly little attention has been devoted to developing systems that can positively impact users’ well-being and health. In an effort to combat the negative effects of a sedentary way of life on people’s health and the subsequent rise in healthcare expenses, we introduce an encouraging approach, i.e. a recommender system that, through webcam-based monitoring of subject postures, suggests personalized exercise breaks to do directly near users’ desks. Our system captures users’ postures during work hours and employs 3D pose estimation to calculate key angles between shoulders, hips, and head. By identifying postural imbalances, we generate exercise recommendations using a Large Language Model (LLM). The system flags potential postural issues when angle thresholds are exceeded and prompts the LLM to provide tailored exercise suggestions. Our method’s effectiveness is assessed by experts in the field. While the results are still preliminary, our approach deserves further investigation, with future developments likely to focus on enriching the data and refining the detection methods. The full-reproducible code is available at the following link: https://github.com/GaetanoDibenedetto/healthrecsys24