A Multi-Sensor Wearable Framework for Retained-Context Worker Risk Assessment Using Event-Triggered Edge AI

This study presents an on-device worker-safety framework that retains resolved primary risks as finite contexts for interpreting later worker behavior. Continuous FFH, workload, and BSEC-derived environmental monitoring is integrated with event-triggered No-motion gating and INT8 worker-state recognition. The selected 4.6-s CNN achieved 98.66% macro-F1 in segment-stratified validation, and nRF5340 replay completed the scheduled 1.2-s inference cycle without deadline misses. We distinguish representative literature-derived P1–P4 paradigms from a controlled integrated P4-I/P5 comparison using identical FFH, gas, workload and worker-state inputs, and identical same-episode decision rules. Across 1212 bounded matched event sequences, policy outputs agreed before episode closure; at 1812 post-closure worker-state checkpoints P5 retained prior-risk evidence, yielding 719 monitoring (R1) and 1093 elevated/critical (R2/R3) outputs, whereas P4-I had closed the earlier episode. A separate finite-state audit enumerated 9134 reachable abstract states and 91,861 admissible transitions. These deterministic findings isolate decision semantics rather than field hazard accuracy or subject-independent generalization. Separately, PPK2 profiling on an nRF5340-based Thingy:53 measured 3.319 versus 2.454 mA (periodic CNN versus CNN-skipped), a 26.05% reduction in the nRF5340 SoC mean current measured at the P2 header when inference was skipped.