English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Circadian rhythm is the natural biological cycle manifested in human dailyroutines. A regular and stable rhythm is found to be correlated with goodphysical and mental health. With the wide adoption of mobile and wearabletechnology, many types of sensor data, such as GPS and actigraphy, provideevidence for researchers to objectively quantify the circadian rhythm of a userand further use these quantified metrics of circadian rhythm to infer theuser's health status. Researchers in computer science and psychology haveinvestigated circadian rhythm using various mobile and wearable sensors inecologically valid human sensing studies, but questions remain whether and howdifferent data types produce different circadian rhythm results whensimultaneously used to monitor a user. We hypothesize that different sensordata reveal different aspects of the user's daily behavior, thus producingdifferent circadian rhythm patterns. In this paper we focus on two data types:GPS and accelerometer data from smartphones. We used smartphone data from 225college student participants and applied four circadian rhythm characterizationmethods. We found significant and interesting discrepancies in the rhythmicpatterns discovered among sensors, which suggests circadian rhythms discoveredfrom different personal tracking sensors have different levels of sensitivityto device usage and aspects of daily behavior.

Circadian Rhythms are Not Captured Equal: Exploring Circadian Metrics  Extracted by Different Computational Methods from Smartphone Accelerometer  and GPS Sensors in Daily Life Tracking