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A Skiing Trace Clustering Model for Injury Risk Assessment

Author

Listed:
  • Milan Dobrota

    (LOGIT d.o.o. Beograd, Belgrade, Serbia)

  • Boris Delibašić

    (Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia)

  • Pavlos Delias

    (Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece)

Abstract

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.

Suggested Citation

  • Milan Dobrota & Boris Delibašić & Pavlos Delias, 2016. "A Skiing Trace Clustering Model for Injury Risk Assessment," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 8(1), pages 56-68, January.
    • Handle: RePEc:igg:jdsst0:v:8:y:2016:i:1:p:56-68
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