ID 2514: EmpkinS D02: Depression Detection from body movement data

Research Project

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 study speech patterns and voice. 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 video data from these interviews to train a deep-learning model that can automatically detect depression. We recommend that you review the following papers to understand the method we are using in our work:

1- ‘SMG: A Micro-Gesture Dataset Towards Spontaneous Body Gestures for Emotional Stress State Analysis’ by Haoyu Chen et al.

2- ‘Mmad: Multi-label micro-action detection in videos’ by Li et al.

Details

• 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 movement.
• You should document your code in a clear and structured manner.
• You will report on your progress weekly and present your results.

Tasks

• Knowledge in data processing, data analysis, and data visualization using Python.
• Knowledge/willingness to learn action recognition.
• Interest in diving into the field of Depression and Psychology
• English proficiency

Supervisors

Amirreza Asemanrafat, M. Sc.

Researcher & PhD Candidate

Please send me an email (amirreza.asemanrafat@fau.de) with your resume and transcripts to apply for the topic. We will then get in contact with you.