FitnessAgent: A Unified Agent Framework for Open-Set and Personalized Fitness Evaluation

Abstract

Robotic systems face challenges in performing open-set and personalized fitness evaluations, especially when adapting to new exercises and individual user needs. This paper introduces FitnessAgent, a unified agent framework designed to address these challenges. Unlike traditional systems that rely on pre-trained neural networks or fixed rule-based criteria, FitnessAgent can assess any exercise without prior training, adapting evaluation metrics based on expert knowledge and user-specific requirements. The system breaks down fitness evaluation tasks into combinations of metrics, each calculated using measurable operators such as angles, distances, and positions. By leveraging a set of primitive, exercise-agnostic operators, a large language model (LLM)-based planner dynamically selects and combines these operators for each task. The open-set capability of FitnessAgent is validated through experiments on both the widely-used Functional Movement Screen dataset and a newly collected isometric pose dataset. Results highlight the system's flexibility in handling new movements and its ability to adapt to personalized evaluation criteria without the need for code or algorithm modifications. FitnessAgent offers a scalable and personalized solution for fitness evaluation, making it well-suited for robotic applications that require adaptability to diverse user needs.