Machine learning-based detection of acute psychosocial stress from body posture and movements.

ABSTRACT: Investigating acute stress responses is crucial to understanding the underlying mechanisms of stress. Current stress assessment methods include self-reports that can be biased and biomarkers that are often based on complex laboratory procedures. A promising additional modality for stress assessment might be the observation of body movements, which are affected by negative emotions and threatening situations. In this paper, we investigated the relationship between acute psychosocial stress induction and body posture and movements. We collected motion data from N = 59 individuals over two studies (Pilot Study: N = 20, Main Study: N = 39) using inertial measurement unit (IMU)-based motion capture suits. In both studies, individuals underwent the Trier Social Stress Test (TSST) and a stress-fr

SUBMITTER: Richer R 

PROVIDER: S-EPMC11375162 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature