An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Gradl S, Wirth M, Richer R, Rohleder N, Eskofier B
Publication year: 2019
Language: English


Abstract

Negative consequences of stress are a pervasive problem in our modern society. Recent developments in wearable lifestyle hardware have led to unobtrusive, sensor-packed, always-on devices that might finally be able to continuously monitor biosignals to detect, determine or even prevent stress or some of its negative outcomes. In this work, we give a concise overview of a majority of biosignals that are in some way relevant for stress classification and outline state-of-the-art machine learning algorithms for this task. Additionally, we provide a list of all recent wearables including an evaluation of their feasibility to implement such algorithms as well as directions to look for an assessment of the accuracy and validity of their recorded data with respect to stress tracking.


FAU Authors / FAU Editors

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Gradl, Stefan
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Richer, Robert
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Rohleder, Nicolas Prof. Dr.
Lehrstuhl für Gesundheitspsychologie
Wirth, Markus
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


How to cite

APA:
Gradl, S., Wirth, M., Richer, R., Rohleder, N., & Eskofier, B. (2019). An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables. In Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '19). Trento, IT.

MLA:
Gradl, Stefan, et al. "An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables." Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '19), Trento 2019.

BibTeX: 

Last updated on 2019-20-05 at 12:38