Seminar/Thesis: machine learning algorithms for context recognition using mobile sensors

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Wearables, including smartwatches, as well as smartphones are equipped with several sensors, which allow monitoring of data relevant to retrieve digital biomarkers. Some of the biomarkers measured by wearables are already common in daily life (e.g. step counters), which foreshadows a future with even more extensive self-quantification in the health sector. To evaluate current possibilities of commercial devices and their sensors, particularly in the field of dietary monitoring, this seminar aims at developing a machine learning pipeline for the development of context recognition systems, which detect daily life situations relevant for dietary assessment. The algorithms will be implemented using pre-existing datasets, and shall be deployed in a smartphone app using e.g. Tensorflow Lite.


Understand the potential of mobile sensors; develop ML algorithm using pre-existing datasets

Learning Objectives:

  • Understand sensor types in smartphones and smartwatches for digital health
  • Develop machine learning algorithm for context recognition
  • Deploy algorithm on a smartphone app


Project type Seminar
ECTS  2.5, 5, 7.5
Language English and/or German
Period Winter term 2021/22
Presence time Virtual seminar, working from remote
Useful knowledge Machine learning, app development, Python, datasets
Work distribution 20% data investigation and literature research 80% programming in Python
Med. Eng. designation Advanced Context Recognition (ACR)
StudOn link Link will follow shortly.
First meeting Online introduction/Vorbesprechung
Registration Via StudOn, obligatory after introduction.



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


Final presentation and final report.


David Kopyto

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
  • Phone number: +49 9131 85-23608
  • Email:

Friedrich-Alexander-Universität Erlangen-Nürnberg