LOPEZ, Guillaume    Department   Aoyama Gakuin University  Department of Integrated Information Technology, College of Science and Engineering    Position   Professor
Language	English
Publication Date	2020/02
Type	Academic Journal
Peer Review	Peer reviewed
Title	The Influence of Person-specific Biometrics in Improving Generic Stress Predictive Models
Contribution Type	Collaboration
Journal	Sensors and Materials
Publisher	MYU K.K.
Volume, Issue, Page	32(2(2)),pp.703-722
Author and coauthor	Kizito Nkurikiyeyezu, Anna Yokokubo, Guillaume Lopez
Details	Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-specific models (i.e., ones that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and are costly to deploy in realworld environments. For illustration, in an office environment, a stress monitoring system that uses person-specific models would require the collection of new data and the training of a new model for every employee. Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and cost-effective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population.
DOI	10.18494/SAM.2020.2650
ISSN	2435-0869