Bachelor/Master Thesis: Diagnosis and Treatment with the OpenEarable

Background

Earables are earbuds that are equipped with multiple sensors. Among them, the OpenEarable (Röddiger et al., 2024) stands out as the device with the most sensors globally, enabling a wide range of interaction possibilities. These capabilities can and have been used to both diagnose and treat different disorders and diseases. Examples include bruxism (Bondareva et al, 2021) or coughing events (Röddiger et al., 2021).

For this thesis, the student is asked to suggest a novel medical use case for the OpenEarable (or its slightly adapted version) in diagnosis, treatment, or related medical applications. The proposed disorder or disease should meet one of the following criteria:

  • Earables have not yet been used (or sufficiently explored) for the given diagnose or treatment.
  • The unique features of the OpenEarable provide clear advantages for the given diagnose or treatment.

A good example for such a thesis is the currently ongoing BA thesis of Felix Schlotter about the detection of TMDs (temporomandibular disorders) through single-sided chewing behavior (see exposé).

The exact configuration and the related tasks of the thesis have to be determined for the specific case of the disorder or disease suggested. A rough guideline of what the student will be asked for is, however, given below:

Tasks

BA/MA:

  1. Developing a diagnosis/treatment framework for the suggested disorder or disease with the OpenEarable.

MA additional:

  1. Implementing and deploying the researched framework directly onto the OpenEarable device.
  2. Showcase the practical potential of the developed diagnosis/treatment through compelling use-case demonstrations.

Requirements

  • Interest in Human-Computer-Interaction (HCI) and the real-world application of new devices
  • Interest in medicine and the application of medical gadgets
  • Good Python skills
  • Optional (mainly for MA)
    • Good C/C++ skills
    • Experience with Arduino

If you are interested in this topic, please contact Valeria Zitz (zitz@teco.edu) and/or Jonas Hummel (hummel@teco.edu).