Authors:
Zilu Liang
1
;
2
;
Huyen Hoang Nhung
2
;
Lauriane Bertrand
3
and
Nathan Cleyet-Marrel
3
Affiliations:
1
Institute of Industrial Science, The University of Tokyo, 113-8654 Tokyo, Japan
;
2
Ubiquitous and Personal Computing Lab, Kyoto University of Advanced Science (KUAS), 621-8555 Kyoto, Japan
;
3
National Institute of Electrical Engineering, Electronics, Computer Science, Hydraulics and Telecommunications (INP- ENSEEIHT), 31000 Toulouse, France
Keyword(s):
Sleep Tracking, Data Mining, Personal Informatics, Ubiquitous Computing, Wearable Computing.
Abstract:
Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features—such as the average and longest diagonal line length derived through the rec
urrent quantitative analysis of the step time series—may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.
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