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, researchers performed an experiment, in which a set of students’ physiological data was gathered throughout three exams. They used a smartwatch-like wearable device and collected multimodal physiological data.
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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.
Ultimately, the researchers believe it would be extremely beneficial to consider how exam performance and the stress that goes along with it interact. It will allow for a wide range of potential applications to enhance personal performance.
This may, for instance, assist scientists in developing effective interventions to improve each person’s performance and increase productivity within a company. Additionally, the knowledge may be used in online and remote learning contexts to connect with students effectively and improve learning outcomes.
Source: Eurekalert
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