Prof. Dr.-Ing. Bert Arnrich

Data Science for Wearables Summer Term 2024

  • Introduction to Data Science for Wearables: Covering essential data science principles and their application in analyzing time-series data from wearables. This includes an overview of wearable technology’s role in health and fitness, alongside statistical foundations for robust data analysis.
  • Statistical Data Analysis and Experimentation: Focusing on designing statistically valid empirical data collection methods with wearables, including conducting experiments and achieving accurate statistical test results.
  • Handling Time-Series Data: Techniques for managing time-series data challenges, such as imputation for missing data and dimensionality reduction, to simplify analysis without losing critical information.
  • Feature Engineering and Machine Learning Basics: Introducing feature extraction methods from raw data and transitioning to machine learning, specifically for tasks like classification and pattern recognition in wearable sensor data.
  • Practical Application with Wearables: Empirical experimentation with wearable devices to apply covered theories in real-world scenarios, enhancing learning through hands-on experience. No prior knowledge required; the course caters to all levels, providing necessary background knowledge.

Course Organisation

  • Lecture: Monday 1.30pm - 3.00pm in G2.U.10-14 (computer pool room in the basement of the Digital Health Center)
  • Tutorial: Tuesday 9.15am - 10.45am in G2.U.10-14

Course Material

Please find all course material on openHPI.


The final grade is composed of three equal parts:

  • Experimental data collection and data analysis: 1/3
  • Technical report: 1/3
  • Presentation: 1/3