A Multi-Agent Digital Twin Framework for AI-Driven Fitness Coaching
Vahdati, Monica Gholizadeh HamlAbadi, Kamran Laamarti, Fedwa El Saddik, Abdulmotaleb
Vahdati, Monica
Gholizadeh HamlAbadi, Kamran
Laamarti, Fedwa
El Saddik, Abdulmotaleb
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
We introduce DTAIFC, a modular Digital Twin AI Fitness Coaching system that delivers personalized feedback through multimodal interaction. The system combines OpenPose-based skeletal tracking with a Crew-inspired multi-agent architecture to analyze user posture and provide biomechanically grounded coaching in natural language and voice. At its core, an Orchestrator Agent coordinates Feedback and Recommendation Agents, leveraging short-term memory (Redis) for real-time session context and long-term memory (PostgreSQL) for user-specific historical insight. Language generation is powered by GPT-4, enabling adaptive, context-aware feedback through prompt-driven reasoning. DTAIFC operates asynchronously through a lightweight web interface, supporting input via static images, voice commands, and text queries. Unlike real-time systems that depend on continuous video or wearables, DTAIFC offers a scalable, privacy-conscious solution for intelligent fitness guidance in virtual environments. This framework establishes a new paradigm for memory-augmented, agentic AI coaching, advancing the integration of digital twins in human-centered applications. © 2025 Copyright held by the owner/author(s).
Citation
M. (Monireh) Vahdati, K. Gholizadeh Hamlabadi, F. Laamarti, and A. El Saddik, “A Multi-Agent Digital Twin Framework for AI-Driven Fitness Coaching,” IMX 2025 - Proceedings of the 2025 ACM International Conference on Interactive Media Experiences, pp. 380–385, May 2025, doi: 10.1145/3706370.3731651
Source
IMX 2025 - Proceedings of the 2025 ACM International Conference on Interactive Media Experiences
Conference
2025 ACM International Conference on Interactive Media Experiences, IMX 2025
Keywords
Agentic AI, Digital Twins, Fitness Coaching, Large Language Model (LLM), Multi-Agent System
Subjects
Source
2025 ACM International Conference on Interactive Media Experiences, IMX 2025
Publisher
Association for Computing Machinery