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

Group Members

 

Students

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.


    • 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 Hirn
      Investigating the Expected Goals Metric using Biomechanical Features gained from Pose Estimation
    • 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
    • Juliana Klein
      Development of Algorithms for Computing Knee Stability Parameters Using a Sensor Equipped Knee Sleeve
    • Lucas Wittmann
      Measuring Motivation in Sports: A Machine Learning Approach to Analyse the Effect of Gamification on Biosignals and Motivation in Sport
    • 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
    • Roobesh Balaji
      A Multimodal Approach to Analyze the Relation Between Motivation and Performance in Soccer
    • Sangeetha Chelottillam
      Machine learning analysis of factors affecting motivation and performance in recreational running
    • Timur Perst
      Transfer Learning for Activity Recognition in Ultimate Frisbee
    • Verena Enzenhöfer
    • Vishaal Saravanan
      Multimodal machine learning for calving detection

     

    Projects

    2025

    2024

    2023

    2022

    2021

    2020

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    2016

    2015