Navigator lookout activity classification using wearable accelerometers

Ik Hyun Youn, Jong Hoon Youn

Research output: Contribution to journalArticlepeer-review


Maintaining a proper lookout activity routine is integral to preventing ship collision accidents caused by human errors. Various subjective measures such as interviewing, self-report diaries, and questionnaires have been widely used to monitor the lookout activity patterns of navigators. An objective measurement of a lookout activity pattern classification system is required to improve lookout performance evaluation in a real navigation setting. The purpose of this study was to develop an objective navigator lookout activity classification system using wearable accelerometers. In the training session, 90.4% accuracy was achieved in classifying five fundamental lookout activities. The developed model was then applied to predict real-lookout activity in the second session during an actual ship voyage. 86.9% agreement was attained between the directly observed activity and predicted activity. Based on these promising results, the proposed unobstructed wearable system is expected to objectively evaluate navigator lookout patterns to provide a better understanding of lookout performance.

Original languageEnglish (US)
Pages (from-to)182-186
Number of pages5
JournalJournal of Information and Communication Convergence Engineering
Issue number3
StatePublished - Sep 1 2017


  • Lookout activity classification
  • Machine learning
  • Maritime information
  • Wearable sensor

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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