Levenshtein distance-based regularity measurement of circadian rhythm patterns

Taek Lee, Hoh In

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we introduce an algorithm and an application for modeling user’s circadian rhythm with activity trackers, also known as smart bands (e.g., Misfit Shine or Fitbit). The proposed algorithm detects anomalies in the user 's circadian rhythm pattern (i.e., activity pattern of 24-hour cycle). Diurnal biorhythm data were collected using smart bands and the data were analyzed using Levenshtein distance. We evaluate the performance of the proposed algorithm to distinguish between ordinary days and abnormal days. During the experiment period, the users recorded the mood, fatigue, and event occurrence of the day, and evaluated the performance of the proposed algorithm through comparison with user’s recorded opinions. In the user study, the proposed method detected normal or abnormal patterns of life rhythm with 86% accuracy.

Original languageEnglish
Pages (from-to)4358-4366
Number of pages9
JournalJournal of Theoretical and Applied Information Technology
Volume95
Issue number18
Publication statusPublished - 2017 Sep 30

Fingerprint

Circadian Rhythm
Regularity
Mood
User Modeling
User Studies
Fatigue
Anomaly
Fatigue of materials
Cycle
Evaluate
Experiment
Experiments

Keywords

  • Activity tracker
  • Anomaly detection
  • Circadian rhythm
  • Pattern modeling
  • Wearable device

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Levenshtein distance-based regularity measurement of circadian rhythm patterns. / Lee, Taek; In, Hoh.

In: Journal of Theoretical and Applied Information Technology, Vol. 95, No. 18, 30.09.2017, p. 4358-4366.

Research output: Contribution to journalArticle

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