Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Data Science for Wearables (Sommersemester 2024)

Lecturer: Prof. Dr. Bert Arnrich (Digital Health - Connected Healthcare) , Orhan Konak (Digital Health - Connected Healthcare)

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.04.2024 - 30.04.2024
  • Examination time §9 (4) BAMA-O: 08.07.2024
  • Teaching Form: Lecture / Exercise
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs, Module Groups & Modules

Data Engineering MA
Digital Health MA
Software Systems Engineering MA
  • MALA: Machine Learning and Analytics
    • HPI-MALA-C Concepts and Methods
  • MALA: Machine Learning and Analytics
    • HPI-MALA-T Technologies and Tools
  • MALA: Machine Learning and Analytics
    • HPI-MALA-S Specialization
  • DSYS: Data-Driven Systems
    • HPI-DSYS-C Concepts and Methods
  • DSYS: Data-Driven Systems
    • HPI-DSYS-T Technologies and Tools
  • DSYS: Data-Driven Systems
    • HPI-DSYS-S Specialization
IT-Systems Engineering MA


Course Content

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Please find more detailed information here



The final grade is composed of three equal parts:

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


Mondays 13:30pm &
Tuesdays 9:15-10:45am
in G2.U10/14