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About the Machine Learning and Data Analytics Lab

The researchers in the Machine Learning and Data Analytics (MaD) lab conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.

Home of the Innovation Lab

The MaD Lab is the home of the Innovation Lab for Wearable and Ubiquitous Computing, where students develop innovative prototypes with industry partners.

The registration for the Innolab in the summer term 2018 starts on the 5th of March! Students can register via Studon!

Recent News

On 2nd and 3rd of July Martin and Till visited the European Falls Festival in Manchester, UK. The European Falls Festival brings together leading academics, researchers, healthcare practitioners, clinicians, industry representatives and key stakeholders from across the globe to celebrate best practi...

Coming from Medical Engineering by education, Philipp already put a focus on data and signal processing during his Bachelor and Master studies. After placing his master thesis project at the MaD Lab in cooperation with the adidas AG in Portland, US he decided to continue his time at the Friedrich Al...

Markus Wirth presented his research work on "Assessment of Perceptual-Cognitive Abilities among Athletes in Virtual Environments: Exploring Interaction Concepts for Soccer Players" @ACM DESIGNING INTERACTIVE SYSTEMS (DIS) Conference 2018 in Hong Kong. Well done! #DIS2018

Congratulations to Eva being on stage at the adidas headquarters giving an amazing talk about Digital Sports and the successful collaboration between the MaD Lab and adidas @faugoesadidas. We were very happy about celebrating the fruitful relationship and the opportunity to give insights into the jo...

Ivanna Timotius presented her research during Measuring Behavior 2018 in Manchester, UK: "Rodent’s Stride Length Depends on Body Size: Implications for CatWalk Assay" and "Systematic Data Analysis and Data Mining in Gait Analysis by Heat Mapping". @mbconference #MB2018 ...

Contact

Irene Steinheimer

  • Organization: Department of Computer Science
  • Address:
    Carl-Thiersch-Straße 2b
    Room 01.017
    91052 Erlangen
  • Phone number: +49 9131 85 28990
  • Fax number: +49 9131 85 28980
  • Email: irene.steinheimer@fau.de