  Circadian rhythm is the natural biological cycle manifested in human daily
routines. A regular and stable rhythm is found to be correlated with good
physical and mental health. With the wide adoption of mobile and wearable
technology, many types of sensor data, such as GPS and actigraphy, provide
evidence for researchers to objectively quantify the circadian rhythm of a user
and further use these quantified metrics of circadian rhythm to infer the
user's health status. Researchers in computer science and psychology have
investigated circadian rhythm using various mobile and wearable sensors in
ecologically valid human sensing studies, but questions remain whether and how
different data types produce different circadian rhythm results when
simultaneously used to monitor a user. We hypothesize that different sensor
data reveal different aspects of the user's daily behavior, thus producing
different circadian rhythm patterns. In this paper we focus on two data types:
GPS and accelerometer data from smartphones. We used smartphone data from 225
college student participants and applied four circadian rhythm characterization
methods. We found significant and interesting discrepancies in the rhythmic
patterns discovered among sensors, which suggests circadian rhythms discovered
from different personal tracking sensors have different levels of sensitivity
to device usage and aspects of daily behavior.
