English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Because stress is subjective and is expressed differently from one person toanother, generic stress prediction models (i.e., models that predict the stressof any person) perform crudely. Only person-specific ones (i.e., models thatpredict the stress of a preordained person) yield reliable predictions, butthey are not adaptable and costly to deploy in real-world environments. Forillustration, in an office environment, a stress monitoring system that usesperson-specific models would require collecting new data and training a newmodel for every employee. Moreover, once deployed, the models would deteriorateand need expensive periodic upgrades because stress is dynamic and depends onunforeseeable factors. We propose a simple, yet practical and cost effectivecalibration technique that derives an accurate and personalized stressprediction model from physiological samples collected from a large population.We validate our approach on two stress datasets. The results show that ourtechnique performs much better than a generic model. For instance, a genericmodel achieved only a 42.5% accuracy. However, with only 100 calibrationsamples, we raised its accuracy to 95.2% We also propose a blueprint for astress monitoring system based on our strategy, and we debate its merits andlimitation. Finally, we made public our source code and the relevant datasetsto allow other researchers to replicate our findings.

The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models