LLM-Powered Personalized Feedback System for Local Muscular Fatigue Monitoring Using Electromyography during Construction Material Handling

Wearable health monitoring technologies empowered by AI and ML are transforming occupational health management. EMG sensors provide continuous muscle activity tracking, helping effectively prevent work-related fatigue. This advancement is vital in construction, where physical strain and accidents threaten worker safety. However, existing data-driven EMG models deliver objective measurements but lack actionable and predictive capabilities. We introduce an intelligent feedback system using a GPT-powered RAG framework that integrates EMG data with expert fatigue-assessment knowledge to deliver personalized, real-time recommendations tailored to tasks and individual conditions. Beyond traditional monitoring, it dynamically assesses muscle fatigue and generates proactive, context-aware guidance to mitigate risks. We validated the system in simulated exoskeleton-assisted material handling within realistic construction environments. Performance was evaluated with RAG metrics—context relevance, answer relevance, and groundedness—and results show improved and effective insights compared to conventional methods. This paradigm establishes a new standard for personalized, AI-driven occupational health in construction.