openTSST – An open web platform for large-scale, video-based motion analysis during acute psychosocial stress

Richer R, Geßler T, Herzer L, Abel L, Küderle A, Rohleder N, Eskofier B (2023)

Publication Language: English

Publication Type: Conference contribution, Abstract of a poster

Publication year: 2023

Event location: MIT Media Lab, Boston, MA US

Abstract

We present openTSST, an open web platform to assess stress-related motion changes from video data with the goal to enable a more holistic stress assessment. Our proof-of-concept study shows that stress-induced changes in body posture and movements can reliably be measured using openTSST. With this work, we lay the groundwork for an ecological and large-scale analysis of human behavior during stress which can contribute to new findings in stress research.

Authors with CRIS profile

Robert Richer Machine Learning and Data Analytics Lab Luca Abel Machine Learning and Data Analytics Lab Arne Küderle Machine Learning and Data Analytics Lab Nicolas Rohleder Lehrstuhl für Gesundheitspsychologie Björn Eskofier Machine Learning and Data Analytics Lab

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung

Related research project(s)

Berührungslose Messung von Stress, seiner Determinanten und Konsequenzen (SFB 1483 EmpkinS D03) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025

How to cite

APA:

Richer, R., Geßler, T., Herzer, L., Abel, L., Küderle, A., Rohleder, N., & Eskofier, B. (2023, October). openTSST – An open web platform for large-scale, video-based motion analysis during acute psychosocial stress. Poster presentation at IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health, MIT Media Lab, Boston, MA, US.

MLA:

Richer, Robert, et al. "openTSST – An open web platform for large-scale, video-based motion analysis during acute psychosocial stress." Presented at IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health, MIT Media Lab, Boston, MA 2023.

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