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Identification and modelling of interaction in volleyball using recognized actions of players

Beenhakker, L. (2020) Identification and modelling of interaction in volleyball using recognized actions of players.

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Abstract:Volleyball is the 5th most popular sport in the world with more than 100,000 players in the Netherlands alone. During a volleyball training, a coach cannot keep an eye on all players and therefore cannot give feedback to everyone on the performance of actions, both on individual level as well as team play/interaction level. This can be solved by using technology and giving sensors to players to recognize actions. These can then be used to recognize team play. This is also the goal of the Smart Sports Exercises (SSE) project as this project aims to support training and coaching in volleyball using an interactive floor and sensors worn by players. Using sensor data, individual actions can already be recognized. Therefore, this thesis focusses on the next step (team play) with as main research question: How can the interaction between volleyball players be identified and modelled based on recognized player actions? In context of this thesis, interaction in volleyball is linked to team play and sequences of actions. These sequences of actions can be used to divide a rally into six different complexes. The complexes represent different parts of a match and have previously been used in combination with Social Network Analysis (SNA) to analyse volleyball matches. As SNA has already been used in volleyball analysis, this thesis introduces Labelled Transition Systems (LTS) as a different approach to analyse volleyball. The LTS is created either by design or data-driven. Data collected during two measurement sessions is used as input for any LTS to update the weights and show an LTS graph to a coach. Models created by design can be used for rallies or training exercises linked to the complexes. With the current approach, models created data-driven can be used if a coach wants to practise an exercise that does not fit within the framework of complexes. Actions are recognized by a previously developed action recognizer which has an Unweighted Average Recall of 67.87%. Errors made by the action recognizer influence the output of the LTS. Using different confusion matrices, the influences of different types of errors are identified and analysed. This results in the requirement of the action recognizer to recognize so-called nonFreeball actions with a recall of at least 95% at the cost of Freeball actions, which can be recognized with a recall of 80%. Using the LTS in the SSE project is an effective way to keep track of the complexes and actions performed by players. The main reason is the fact that there are two ways to create a model and therefore these models can be used to analyse interaction in both training settings as well as during matches. The interactive floor, which is part of the SSE project, can be used to give feedback about the complexes to the players, but there is some delay in recognizing the current state. Future research can focus on improving the LTS to accommodate to more actions like an action in which players try to block (but fail) or an action like a fake smash. Other directions can focus on feedback given to players and coaches or the improvement of the action recognizer.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Programme:Biomedical Engineering MSc (66226)
Link to this item:http://purl.utwente.nl/essays/80724
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