Digital Health – PsychoSense
The Digital Health – PsychoSense research group aims to explore the potential of digital technologies in psychological research to better understand human psychology and behavior. Many psychological research areas still rely on “traditional” methods, such as laboratory protocols or the collection of self-reports or obtrusive, often invasive biomarkers which lack of digital solutions and might limit the advancements of these areas. Thus, our goal is to tackle this issue by working at the intersection of technology, health, and psychology. The research projects of our group include the development of mobile apps, wearable and contactless sensing paradigms, machine learning algorithms, or other digital platforms that can assist in the assessment, induction, or intervention of various psychological states. We also explore the use of virtual reality, biofeedback, and other novel technologies for enhancing emotional regulation, stress management, or cognitive performance.
In addition, we are strong advocates for open science and adopt a range of open science practices in our research. We want our research to be reproduced, built upon, and used in final applications. Thus, we develop and publish open-source software frameworks (for example, have a look here, here, or here) that enable other researchers to actually use our work to facilitate and advance their own research. Additionally, we also make our datasets and corresponding analyses available, aiming to help promote scientific rigor, reduce the risk of bias, and enable others to build on our group’s work. By embracing the principles of open science, our research group is contributing to a broader movement within the scientific community that seeks to promote greater transparency and accountability in research, while also promoting collaboration and innovation in scientific research.
Group Head
Group Members
Our Students
Current Students
Marie Oesten
Radar-Based Investigation of Pulse Wave and Heart Sound Propagation
Vivien Holzwarth Correa
Classification and assessment of rheumatoid arthritis from motion capture data using machine learning
Student Assistants
Past Students
- Benjamin Zenke
stress+ – Development and Evaluation of a Web Application for Remote, Internet-based Induction of Acute Psychosocial Stress - Clark Bäker
Improving the robustness of radar-based heart sound detection - Daniel Krauß
Benchmarking of Sleep/Wake Detection Algorithms using Wearable Sensors and Machine Learning - Friederike Popp
Towards frailty assessment in older adults: Investigation of sit-to-stand transfer detection using ear-worn sensors in real-world activities - Jana Reinwald
Triggering an endocrine stress response in the Virtual Reality Stroop Room through social-evaluative stress - Luca Abel
Machine Learning-Based Detection of Acute Psychosocial Stress from Dynamic Movements - Paula Kaiser
Influence of Social-Evaluative Stress in the Virtual Reality Stroop Room - Philipp Dörfler
Comparison of Algorithms for Respiratory Information Extraction from Wearable Sensors - Sebastian Stühler
Investigation of the Pre-Ejection Period as a Marker for Sympathetic Activity during Acute Psychosocial Stress - Tobias Gessler
OpenTSST – An Open Web Platform for Large-Scale, Video-Based Motion Analysis During Acute Psychosocial Stress - Ulla Sternemann
Extraction of Pre-Ejection Period as Marker for Acute Psychosocial Stress from Wearable Sensors and Interferometry Radar - Veronika Koch
Influence of Acute Psychosocial Stress on Body Posture and Movement
Projects
- Empathokinesthetic sensors for biofeedback in depressed patients (D02)
- Contactless measurement of stress, its determinants, and consequences (D03)
- Machine Learning in Health Psychology
- Novel Methods for (Remote) Acute Stress Induction
- Interventions for Acute Psychosocial Stress Responses
- BioPsyKit – A Python Package for the Analysis of Biopsychological Data
- CARWatch – An Open-Source Framework for Improving Cortisol Awakening Response Sampling
