JumpXClass: Explainable AI for Jump Classification in Trampoline Sports
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Movement patterns in trampoline gymnastics have become faster and more complex with the increase in the athletes’ capabilities. This makes the assessment of jump type, pose, and quality during training or competitions by humans very difficult or even impossible. To counteract this development, data-driven solutions are thought to be a solution to improve training. In recent work, sensor measurements and machine learning is used to automatically predict jumps and give feedback to the athletes and trainers. However, machine learning models, and especially neural networks, are black boxes most of the time. Therefore, the athletes and trainers cannot gain any insights about the jump from the machine learning-based jump classification. To better understand the jump execution during training, we propose JumpXClass: a tool for automatic machine learning-based jump classification with explainable artificial intelligence. Using elements of explainable artificial intelligence can improve the training experience for athletes and trainers. This work will demonstrate a live system capable to classify and explain jumps from trampoline athletes.
Details
Original language | English |
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Title of host publication | Datenbanksysteme fur Business, Technologie und Web, BTW 2023 |
Editors | Birgitta Konig-Ries, Stefanie Scherzinger, Wolfgang Lehner, Gottfried Vossen |
Publisher | Gesellschaft fur Informatik (GI) |
Pages | 651-656 |
Number of pages | 6 |
ISBN (electronic) | 9783885797258 |
Publication status | Published - 6 Mar 2023 |
Peer-reviewed | Yes |
External IDs
Scopus | 85149960459 |
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dblp | conf/btw/WoltmannFHL23 |
Mendeley | b8564364-7ffb-3b2d-9a60-981793e061b5 |
ORCID | /0000-0001-8107-2775/work/142253564 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
ASJC Scopus subject areas
Keywords
- applied AI, explainable AI, machine learning, sports, trampoline