  Stress impacts our physical and mental health as well as our social life. A
passive and contactless indoor stress monitoring system can unlock numerous
important applications such as workplace productivity assessment, smart homes,
and personalized mental health monitoring. While the thermal signatures from a
user's body captured by a thermal camera can provide important information
about the "fight-flight" response of the sympathetic and parasympathetic
nervous system, relying solely on thermal imaging for training a stress
prediction model often lead to overfitting and consequently a suboptimal
performance. This paper addresses this challenge by introducing ThermaStrain, a
novel co-teaching framework that achieves high-stress prediction performance by
transferring knowledge from the wearable modality to the contactless thermal
modality. During training, ThermaStrain incorporates a wearable electrodermal
activity (EDA) sensor to generate stress-indicative representations from
thermal videos, emulating stress-indicative representations from a wearable EDA
sensor. During testing, only thermal sensing is used, and stress-indicative
patterns from thermal data and emulated EDA representations are extracted to
improve stress assessment. The study collected a comprehensive dataset with
thermal 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%. Extensive
evaluations highlight ThermaStrain's effectiveness in recognizing
stress-indicative attributes, its adaptability across distances and stress
scenarios, real-time executability on edge platforms, its applicability to
multi-individual sensing, ability to function on limited visibility and
unfamiliar conditions, and the advantages of its co-teaching approach.
