A Skiing Trace Clustering Model for Injury Risk Assessment
| Autor: | Milan Dobrota, Pavlos Delias, Boris Delibasic |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
Relation (database) Computer science business.industry Process mining 02 engineering and technology Machine learning computer.software_genre Spectral clustering Determining the number of clusters in a data set 020204 information systems Modeling and Simulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Risk assessment Cluster analysis business human activities computer Simulation Event (probability theory) TRACE (psycholinguistics) |
| Zdroj: | International Journal of Decision Support System Technology. 8:56-68 |
| ISSN: | 1941-630X 1941-6296 |
| DOI: | 10.4018/ijdsst.2016010104 |
| Popis: | This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. The goal was to achieve the best possible discrimination. Several experimental settings were made to achieve and suggest a good combination of algorithm parameters and cluster number. After clusters are obtained, they are categorized in three categories according to risk level. It can be concluded that the proposed method can be used to distinguish skiing patterns by risk category based on injury occurrences. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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