Sports Analytics

The Sports Analytics group applies different methods in the fields of Machine Learning, Signal Processing, Wearables and Human-Computer Interaction to analyze and predict human motion and performance. To gain deeper insights into the behaviour of athletes in specific sports like running, soccer or volleyball, we conduct in-the-wild and lab studies using inertial measurement units (IMUs), motion capture systems, video, and extended realities. The group also utilizes extended realities to simulate training scenarios and applies them to various fields of application like therapy or performance improvement. Our research contributes to the development of more precise analysis tools in sports and rehabilitation and thus makes the assessment and training more efficient. This can lead to an increase in performance but also help to recognize harmful movement patterns for the prevention of injuries.


Group Head

Dr.-Ing. Eva Dorschky

Room: Room 01.024

Group Members



If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.

  • Lucas Wittmann
    Measuring Motivation in Sports: A Machine Learning Approach to Analyse the Effect of Gamification on Biosignals and Motivation in Sport
  • Roobesh Balaji
    A Multimodal Approach to Analyze the Relation Between Motivation and Performance in Soccer
  • Vishaal Saravanan
    Multimodal machine learning for calving detection

Past Students

  • Antonia Deyerberg
    Unsupervised Personalization of Activity Recognition in Football
  • Antonia Steger
    The Mobile VR-Amblyopia Trainer. An Android Based VR-Game for the Treatment of Amblyopia.
  • Birte Höft
    Implementing an augmented reality application to improve stereoscopic vision
  • Fabian Löbel
    Comparing interaction modalities in eye-tracking based perimetry using a Virtual Reality headset.
  • Florian Schleicher
    Color based BCI Interaction - Classification of Color based on Power Bands from EEG Data
  • Franka Risch
    Measurement of Ocular Deviation with an VR Hess Screen Test Using Eye Tracking
  • Jaromir Vogt
    VR-based tool for mTBI detection using stereopsis performance and eye-tracking data
  • Luis Durner
  • Lukas Heine
    AI – driven golf swing analysis and improvement
  • Oliver Korn
    (Re-)Eye-dentification based on pupillometry data from eye trackers in a VR environment
  • Robin Modisch
    Immersive Gesture-Based Human Robot Interaction in MR
  • Roland Stolz
    Athlete Identification and Personel Profile Creation based on Movement Sensor Data
  • Timur Perst
    Transfer Learning for Activity Recognition in Ultimate Frisbee
  • Verena Enzenhöfer