Thesis supervisor: György Cserey
co-supervisor: László Grand
Location of studies (in Hungarian): PPKE Abbreviation of location of studies: PPKE
Description of the research topic:
"Wearable sensors and computer vision are increasingly being utilized in various places to measure individual athlete activity, optimize it, prevent injuries, and assess team-level performance while determining optimal strategies. The efficient processing and accurate interpretation of this data are of utmost importance for amateur and professional athletes, coaches, teams, and the audience.
The tasks primarily revolve around two aspects: firstly, the collection of data from athletes through wearable sensors. From these data, important performance-related events are detected, and their descriptions are generated using appropriate statistics and machine learning processes to achieve optimal performance. Time series data can also be evaluated using statistical, probabilistic, machine learning, and deep learning methodologies.
Machine vision research tasks can be applied in both rehabilitation and sports contexts. These tasks include the automatic extraction of visual content, estimation of human body positions using deep learning models, and research into machine learning algorithms necessary for rehabilitation or sports activity optimization.
The fusion of wearable sensors and machine learning enables the implementation of new system architectures and the analytical integration of the researched methodologies, making it possible to carry out research tasks related to rehabilitation or sports activities effectively."
Required language skills: English (B2) Recommended language skills (in Hungarian): English Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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