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  1. Free, publicly-accessible full text available June 1, 2025
  2. 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. These evaluations validate ThermaStrain’s fidelity and its potential for enhancing stress assessment. 
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    Free, publicly-accessible full text available October 15, 2024
  3. Free, publicly-accessible full text available October 8, 2024
  4. Free, publicly-accessible full text available August 1, 2024
  5. Knowing how and when people interact with their surroundings is crucial for constructing dynamic and intelligent environments. Despite the importance of this problem, an attainable and simple solution is still lacking. Current solutions often require powered sensors on monitored objects or users themselves. Many such systems use batteries [1-3], which are costly and time consuming to replace. Some powered systems connect to the grid, which may save swapping batteries, but at the price of restricted placement options. Other solutions use passive tags on monitored objects or require no tags at all, but many of these systems have prohibitive characteristics. For instance, camera-based systems [4,5] generally will not work if their view is occluded. Many other systems that rely on passive tags or do not use tags require direct line-of-sight or close proximity to work. As such, our goal was to design and develop small, cheap, easy-to-install tags that do not require any batteries, silicon chips or discrete electronic components, which can be monitored without direct line-of-sight.

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