Edge-AI Enabled Wearables for Construction Safety: Real-Time Physiological Monitoring and Localised Data Processing

This paper presents the design, implementation, and controlled evaluation of a proof-of-concept ear-level wearable system that integrates local artificial intelligence for real-time physiological monitoring in construction safety applications. The proposed architecture combines photoplethysmography (PPG), non-contact infrared thermometry, and nine-axis inertial sensing on a Raspberry Pi Pico microcontroller, enabling local inference that reduces dependence on cloud processing. A lightweight logistic regression model with three binary outputs, trained on a subset of the publicly available WESAD dataset (subjects S2–S4), classifies three physiological states relevant to worker safety—elevated PPG variability, drowsiness, and fatigue—directly from the device’s 2 MB flash memory. The principal contribution is demonstrating that ear-level multi-sensor fusion combined with on-device machine learning achieves high agreement with clustering-derived proxy labels under controlled conditions (average F1-score: 97.80% on an unseen test subject) while sustaining sub-second inference latency (<0.5 s). These results support timely supervisor alerting and motivate subsequent field validation in operational construction environments.