Agent-to-Agent (A2A) Protocol Integrated Digital Twin System with AgentIQ for Multimodal AI Fitness Coaching and Personalized Well-Being

摘要

AI-based fitness coaching systems are typically monolithic and opaque, limiting adaptability, transparency, and embodied interaction. We propose a protocol-integrated Digital Twin (DT) architecture that reimagines fitness coaching as a distributed, explainable, and emotionally adaptive ecosystem. The framework adopts a CrewAI-inspired multi-agent design, where specialized agents for posture analysis, speech, physiological sensing, and personalized recommendation collaborate through the Agent-to-Agent (A2A) protocol to enable secure and interoperable task delegation. Context is maintained through short- and long-term memory modules, while the Model Context Protocol (MCP) supports flexible tool and model invocation across heterogeneous AI resources. Transparency and efficiency are ensured with NVIDIA AgentIQ and LangSmith, which provide token-level observability, workflow profiling, and trajectory evaluation. Real-time coaching feedback is synthesized into multimodal outputs, text, speech, and embodied avatars, using Audio2Face and Omniverse, creating expressive and emotionally engaging interactions. This paper presents a blueprint for protocol-driven, multimodal DTs in health and well-being. By combining interoperability, observability, and embodied feedback, it advances beyond centralized assistants toward distributed, memory-aware, and emotionally intelligent digital coaches, laying the foundation for next-generation human-AI collaboration in multimedia health applications.