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

 

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.

  • 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

     

    Projects

    Publications

    2024

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