ID 2334: Depression detection using multi-domain features from motion sensors and voice

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Cloud Hidden Dilemma Depression BlissRawpixel Ltd.

Master’s Thesis / Project / Research Internship

Depression is a common mental health disorder that often goes undetected or is diagnosed at a late stage, leading to long periods of suffering for individuals affected. Early identification allows for prompt intervention and treatment that can alleviate symptoms and improve overall mental well-being. Recent advancements in wearable technology have opened new possibilities for early and objective methods of depression detection.


The goal of this thesis is to design a multi-domain system to detect depression by combining motion data from ear-worn IMUs and voice features. A study to collect a representative data set should be planned and conducted. All developed algorithms should be data-driven and the impact of individual features as potential markers of depression should be analyzed.

Start Date: Sommer/Fall 2023

Ideally in combination with a research project/internship (2 month research internship, 6 month thesis).


  • Literature research
  • Data recording
  • Process data and extras relevant features for motion data and voice
  • Design a system for the detection of depression based on motion and voice features
  • Bring in your own ideas


  • Fluent German skills (required for data collection)
  • You should have empathy and show a lot of initiative
  • Strong programming skills in Python
  • Advanced knowledge and some experience in Machine Learning
  • Advanced knowledge in signal processing
  • Bachelor’s degree in a related field of study


Ann-Kristin Seifer, M. Sc.

Researcher & PhD Candidate

Please use the application form to apply for the topic. We will then get in contact with you.