  Human activities follow daily, weekly, and seasonal rhythms. The emergence of
these rhythms is related to physiology and natural cycles as well as social
constructs. The human body and biological functions undergo near 24-hour
rhythms (circadian rhythms). The frequency of these rhythms is more or less
similar across people, but its phase is different. In the chronobiology
literature, based on the propensity to sleep at different hours of the day,
people are categorized into morning-type, evening-type, and intermediate-type
groups called \textit{chronotypes}. This typology is typically based on
carefully designed questionnaires or manually crafted features drawing on data
on timings of people's activity. Here we develop a fully data-driven
(unsupervised) method to decompose individual temporal activity patterns into
components. This has the advantage of not including any predetermined
assumptions about sleep and activity hours, but the results are fully
context-dependent and determined by the most prominent features of the activity
data. Using a year-long dataset from mobile phone screen usage logs of 400
people, we find four emergent temporal components: morning activity, night
activity, evening activity and activity at noon. Individual behavior can be
reduced to weights on these four components. We do not observe any clear
emergent categories of people based on the weights, but individuals are rather
placed on a continuous spectrum according to the timings of their activities.
High loads on morning and night components highly correlate with going to bed
and waking up times. Our work points towards a data-driven way of categorizing
people based on their full daily and weekly rhythms of activity and behavior,
rather than focusing mainly on the timing of their sleeping periods.
