FlexAI: A Multi-modal Solution for Delivering Personalized and Adaptive Fitness Interventions

Abstract: Personalization of exercise routines is a crucial factor in helping people achieve their fitness goals. Despite this, many contemporary solutions fail to offer real-time, adaptive feedback tailored to an individual's physiological states. Contemporary fitness solutions often rely only on static plans and do not adjust to factors such as a user's pain thresholds, fatigue levels, or form during a workout routine. This work introduces FlexAI, a multi-modal system that integrates computer vision, physiological sensors (heart rate and voice), and the reasoning capabilities of LLMs to deliver real-time, personalized workout guidance. FlexAI continuously monitors a user's physical form and level of exertion, among other parameters, to provide dynamic interventions focused on exercise intensity, rest periods, and motivation. To validate our system, we performed a technical evaluation confirming our models' accuracy and quantifying pipeline latency, alongside an expert review where certified trainers validated the correctness of the LLM's interventions. Furthermore, in a controlled study with 25 participants, FlexAI demonstrated significant improvements over a static, non-adaptive control system. With FlexAI, users reported significantly greater enjoyment, a stronger sense of achievement, and significantly lower levels of boredom and frustration. These results indicate that by integrating multi-modal sensing with LLM-driven reasoning, adaptive systems like FlexAI can create a more engaging and effective workout experience. Our work provides a blueprint for integrating multi-modal sensing with LLM-driven reasoning, demonstrating that it is possible to create adaptive coaching systems that are not only more engaging but also demonstrably reliable.