A machine learning model combining behavioral and physiological features from wearable sensors and smartphones achieved 84.85% accuracy for recognizing general stress and 71.33% for eustress.
Observational (n=6)
No
Can behavioral and physiological features from wearable sensors and devices accurately differentiate eustress from general stress?
Behavioral and physiological data from wearables and devices can help differentiate eustress from general stress with moderate to high accuracy.
Eustress is literally the "good stress" that associated with positive feelings and health benefits. Previous studies focused on general stress, where the concept of eustress has been overlooked. This paper presents a novel approach towards stress recognition using data collected from wearable sensors, smartphones, and computers. The main goal is to determine if behavioral factors can help differentiate eustress from another kind of stress. We conducted a natural experiment to collect user smartphone and computer usage, heart rate and survey data in situ. By correlation and principle component analysis, a set of features could then be constructed. The performance was evaluated under leave-one-subject-out cross-validation, where the combined behavioral and physiological features enabled us to achieve 84.85% accuracy for general stress, 71.33% one kind of eustress as an urge for better performance, and 57.34% for eustress as a state of better mood. This work provided an encouraging result as an initial study for measuring eustress.
Li et al. (Mon,) conducted a observational in Perceived stress and eustress (n=6). Machine learning classification using behavioral and physiological features was evaluated on Classification accuracy for general stress. A machine learning model combining behavioral and physiological features from wearable sensors and smartphones achieved 84.85% accuracy for recognizing general stress and 71.33% for eustress.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: