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.

中文翻译：

---

一种基于非负矩阵分解的方法，用于使用手机数据量化活动和睡眠节奏和时间类型

人类活动遵循每日、每周和季节性的节奏。这些节律的出现与生理和自然周期以及社会结构有关。人体和生物功能经历接近 24 小时的节律（昼夜节律）。这些节奏的频率在不同人之间或多或少相似，但其相位不同。在时间生物学文献中，根据一天中不同时间的睡眠倾向，人们将人分为早晨型、傍晚型和中间型组，称为 \textit{chronotypes}。这种类型通常基于精心设计的调查问卷或根据人们活动时间的数据手工制作的特征。在这里，我们开发了一种完全数据驱动（无监督）的方法，将单个时间活动模式分解为组件。这样做的优点是不包括任何关于睡眠和活动时间的预定假设，但结果完全取决于上下文，并由活动数据的最突出特征决定。使用来自 400 人的手机屏幕使用日志长达一年的数据集，我们发现了四个紧急时间组件：早上活动、夜间活动、晚上活动和中午活动。个人行为可以简化为这四个组成部分的权重。我们没有观察到任何基于权重的明确涌现的人类别，而是根据他们的活动时间将个人置于一个连续的范围内。早晚组件的高负荷与就寝和起床时间高度相关。