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 language | English |
|---|---|
| Pages (from-to) | 4358-4366 |
| Number of pages | 9 |
| Journal | Journal of Theoretical and Applied Information Technology |
| Volume | 95 |
| Issue number | 18 |
| Publication status | Published - 2017 Sept 30 |
Bibliographical note
Funding Information:The corresponding author of this paper is Hoh Peter In ([email protected]), and we declare that this paper is an improved version of our preliminary study [14] that had been previously introduced in a conference. This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HI14C3212).
Keywords
- Activity tracker
- Anomaly detection
- Circadian rhythm
- Pattern modeling
- Wearable device
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science