Stress has a negative impact on physical health, reduces work productivity, and results in significant annual costs for industries and healthcare. While high stress is known to raise the risk of cardiovascular disease and have negative effects on mental health, It also has key effects on the ability of one to complete tasks by both excessively high or excessively low stress. There has been growing research interest on understanding how real-world stress impacts our body and performance, at work and across life activities
Unfortunately, attempts to simulate their impact in the laboratory or elsewhere are less useful than datasets gathered in real-world circumstances. As a result, researchers have access to fewer real-world stress datasets. Even rarer indeed are such datasets used in longitudinal investigations on the same subjects over time.
Real-world situations are also unrestricted environments. Research-grade equipment is frequently inaccessible, and motion artifact contamination is pervasive. These continue to be some of the biggest barriers to automated emotion decoders outside of the research labs in daily life.
To address the above-mentioned gap, Rose Faghih and her former PhD students Md. Rafiul Amin and Dilranjan Wickramasuriya performed an experiment, in which a set of students' physiological data was gathered over the course of three exams. They used a smartwatch-like wearable device and collected multimodal physiological data. The use of the smartwatch-like wearable device was to provide a seamless data collection experience for the students participating in the experiment.
The investigation shows that it is possible to link the variations in the physiological signals to the exam performance. More details about this study can be found in the corresponding publication titled "A Wearable Exam Stress Dataset for Predicting Grades using Physiological Signals."
