ID2505: EmpkinS D02: Depression Detection from Body Movements
Depression is one of the most common mental health disorders, significantly reducing an individual’s quality of life. Psychologists use various methods to detect and assess depression, such as analysing facial expressions, speech patterns, and head movements. In this research, our goal is to detect depression using upper body expressions and movements. This study is part of a more extensive series of research on depression, which has gathered data from interviews with over two hundred participants, including both depressed individuals and a control group. Specifically, we aim to use Kinect Azure data from these interviews to train a deep-learning model that can automatically detect depression.
Here, you can find more information about the D02 and EmpkinS projects.
This paper explains our dataset:
Keinert, Marie, et al. “Facing depression: evaluating the efficacy of the EmpkinS-EKSpression reappraisal training augmented with facial expressions–protocol of a randomized controlled trial.” BMC psychiatry 24.1 (2024): 1-15.
This is the most relevant research to our work:
Li, Xingyun, et al. “TSFFM: Depression detection based on latent association of facial and body expressions.” Computers in Biology and Medicine 168 (2024): 107805.
Tasks
- You will work with interdisciplinary researchers and should be ready to research topics outside your field.
- You will perform statistical and machine learning data analysis on the pose data set.
- You should document your code in a clear and structured manner.
- You will report on your progress weekly and present your results and work several times to the MaD lab.
Requirements
- Knowledge in data processing, data analysis, and data visualization using Python.
- Knowledge/willingness to learn Motion analysis.
- Interest in diving into the field of Depression and Psychology
- English proficiency
Supervisors
Please use the application form to apply for the topic. We will then get in contact with you.