Wearable Sensor Fusion for Psychophysiological Arousal Recognition in Air Traffic Control: A Fuzzy MCDM Approach.

Recognizing psychophysiological arousal and strain from wearable sensors extends affective computing beyond discrete-emotion classification. Vigilance decrement during air traffic control (ATC) shifts illustrates this: the psychomotor vigilance task (PVT) cannot be administered while a controller is working. We develop and evaluate a wearable, label-free Vigilance/Strain Risk Index (F-VRI) for ATC operators, fusing five physiological signals via entropy-weighted fuzzy TOPSIS. The index was assessed in two samples: 38 controllers measured pre/post shift (preliminary, pilot-level evidence) and 13 trainees pre/post across simulator sessions (confirmatory validation). Compared with PVT, the fused index detected the pre/post change more reliably at the group level (simulator: p = 0.004 vs. p = 0.379) and at the individual level (68-80% directional concordance vs. 33-55% for PVT, at or below chance). Compared with five supervised machine learning baselines, the fuzzy index was the only method to remain significant under cross-domain transfer (both p < 0.05), while every supervised model failed in at least one direction. An exploratory extension revealed a weighting pathology in which static covariates absorbed disproportionate model weight, which led us to a two-layer state/trait architecture. These results support fuzzy MCDM as an interpretable sensor-fusion framework for psychophysiological state monitoring under uncertainty, with continuous, streaming deployment remaining a prospective extension requiring further validation; the field-cohort effect (p = 0.084) remains at the pilot level.