Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 Transparent, Cost-Sensitive Threshold Calibration for Autonomous Edge-AI Health Wearables: A Severity-Stratified Hysteretic Algorithm for Predictive Asthma Facemasks 70 92 108916 10.22059/jitm.2026.108916 EN Tarun T School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India. Vijay Kumar Garg School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India. Varsha Sahni School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India. V Sharma Department of Computer Science & Engineering, Graphic Era Hill University, Dehradun, India. S Vats Computer Vision Laboratory, Department of Computer Science & Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh - 273010, India. Journal Article Organizations deploying autonomous, AI-enabled health wearables must govern the opaque decision rules that gate their interventions, which carry direct consequences for cost, safety, and accountability. Wearable predictive devices for asthma combine environmental and physiological sensing with on-device actuation, yet these decision thresholds are almost always fixed heuristics. Fixed thresholds ignore inter-patient severity, trade sensitivity against false alarms suboptimally, and cause actuator chattering that wastes battery and metered medication. This paper presents AeroGate, a single-threshold calibration algorithm for a smart, AI-enabled asthma facemask that warms inhaled air, classifies cough acoustics, and triggers a vibrating-mesh nebulizer. AeroGate derives a population severity prior from chest imaging and converts a calibrated risk score into per-patient actuation thresholds through imaging-driven severity stratification, a cost-sensitive base threshold that weighs a missed attack against a false alarm, a data-sized hysteresis band that suppresses chattering, and an O(1) on-device update that holds a target alarm budget. Evaluated on the NIH ChestX-ray14 benchmark (112,120 studies) with strict patient-wise splits, the calibrated head attains an AUC of 0.68 and a Brier score of 0.23; AeroGate reduces actuator toggling by 77% relative to a single-threshold policy while matching detection sensitivity and holds the realised alarm rate at its target budget. Beyond the engineering, AeroGate reframes the operating point as a transparent, auditable management lever: the alarm budget becomes a cost-and-service target that managers can hold, and the cost ratio becomes a risk-tolerance input that clinicians and managers can set, yielding a principled, low-infrastructure recipe for deploying, governing, and scaling autonomous edge-AI health interventions. https://jitm.ut.ac.ir/article_108916_e8e59f2b4f3c9aa221f3cf1d6c11fddf.pdf