Seminar: COVID-19 – Digital Symptom Tracker

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COVID-19 has forced the world into social distancing and self-isolation, and with the general goal of not saturating the healthcare system, establishing an adequate threshold for when to contact the doctor is difficult. Some people may be overreacting to any minor symptom, while other people might be waiting way too long before getting medical advice. As the lockdown continues, it becomes harder to remember when symptoms first appear and their severity across multiple days.


The goal of the project is to create a user-friendly symptom tracking application. The core focus is the UI design. The app should provide an engaging interface to track symptoms and to remind users to take respective measurements, e.g., temperature, and when to contact the house doctor. The app should be knowledge-based so that privacy is preserved by not sending information to any central server.

Learning Objectives

  • Gain an overview of UI design frameworks.
  • Understand expert systems concepts.
  • Apply rule-based programming to symptom tracking.
  • Create and evaluate an app to track symptoms.

Course Data

Project type Seminar (optional: Bachelor or Master thesis)
Language English
Period Winter term 2020/21
Presence time Virtual seminar, working from remote.
Useful knowledge Python, data analytics
Work distribution 40% algorithm development, 30% data analysis and evaluation, 20% consultation, 10% reporting
Med. Eng. designation Advanced Context Recognition (ACR)
StudOn link Please join
First meeting online-introduction-vorbesprechung-of-winter-term-2020-seminars, on 4th November 2020 at 16:15
Registration Via StudOn, obligatory after introduction.


Up-to-date literature recommendations are provided during the lectures.


  • Final project presentation, demonstrator and final report.


Dr. Luis I. Lopera G.

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
  • Phone number: +49 9131 85-23605
  • Email:

Friedrich-Alexander-Universität Erlangen-Nürnberg