Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.

ABSTRACT:

Background

Early detection of elevated acute stress is necessary if we aim to reduce consequences associated with prolonged or recurrent stress exposure. Stress monitoring may be supported by valid and reliable machine-learning algorithms. However, investigation of algorithms detecting stress severity on a continuous scale is missing due to high demands on data quality for such analyses. Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. We aimed to detect laboratory-induced stress using multimodal data and identify challenges researchers may encounter when conducting a similar study.

Methods

We conducted a preliminary exploration of performance of a machine-learning algorithm trained on multimod

SUBMITTER: Ciharova M 

PROVIDER: S-EPMC12203116 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature