Arquitetura de Aprendizado Federado para Wearables: Detecção de Risco com FC e HRV na Indústria - Campos, Bruno; Oliveira, Ricardo Augusto Rabelo (Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2025)
Monitoring physiological stress in industrial and mining workers is critical for occupational safety. Wearable devices enable real-time measurement of heart rate (HR) and heart rate variability (HRV), but data privacy regulations impose severe restrictions. This work proposes a federated learning (FL) architecture where wearable devices train local models using HR and HRV, adjusted by the worker’s age. Only model parameters are shared with a central server, preserving privacy. Alerts are issued locally when HR exceeds 85% of the age-adjusted maximum heart rate (FC max = 208 − 0.7 × age) or HRV drops below 20 ms. A proof-of-concept implementation demonstrates the feasibility of decentralized health risk detection using real medical thresholds, without exposing raw physiological data.