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Stress impacts our physical and mental health as well as our social life. Apassive and contactless indoor stress monitoring system can unlock numerousimportant applications such as workplace productivity assessment, smart homes,and personalized mental health monitoring. While the thermal signatures from auser's body captured by a thermal camera can provide important informationabout the "fight-flight" response of the sympathetic and parasympatheticnervous system, relying solely on thermal imaging for training a stressprediction model often lead to overfitting and consequently a suboptimalperformance. This paper addresses this challenge by introducing ThermaStrain, anovel co-teaching framework that achieves high-stress prediction performance bytransferring knowledge from the wearable modality to the contactless thermalmodality. During training, ThermaStrain incorporates a wearable electrodermalactivity (EDA) sensor to generate stress-indicative representations fromthermal videos, emulating stress-indicative representations from a wearable EDAsensor. During testing, only thermal sensing is used, and stress-indicativepatterns from thermal data and emulated EDA representations are extracted toimprove stress assessment. The study collected a comprehensive dataset withthermal video and EDA data under various stress conditions and distances.ThermaStrain achieves an F1 score of 0.8293 in binary stress classification,outperforming the thermal-only baseline approach by over 9%. Extensiveevaluations highlight ThermaStrain's effectiveness in recognizingstress-indicative attributes, its adaptability across distances and stressscenarios, real-time executability on edge platforms, its applicability tomulti-individual sensing, ability to function on limited visibility andunfamiliar conditions, and the advantages of its co-teaching approach.

"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection