论文标题
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以及计时型的节奏
A Non-negative Matrix Factorization Based Method for Quantifying Rhythms of Activity and Sleep and Chronotypes Using Mobile Phone Data
论文作者
论文摘要
人类活动每天，每周和季节性节奏。这些节奏的出现与生理和自然周期以及社会结构有关。人体和生物学功能接近24小时的节奏（昼夜节律）。这些节奏的频率或多或少是在人们之间的相似之处，但其阶段是不同的。在《年级生物学文献》中，基于一天中不同时间睡觉的倾向，人们被归类为早晨类型，夜间类型和中等类型的群体，称为\ textit {chronotypes}。该类型学通常基于精心设计的问卷或手动制作的功能，这些功能利用人们活动时间的数据。在这里，我们开发了一种完全数据驱动的（无监督）方法，将单个时间活动模式分解为组件。这具有不包括关于睡眠和活动时间的任何预定假设的优点，但是结果完全依赖于上下文，并取决于活动数据的最突出特征。使用400人的手机屏幕使用日志中的长达一年的数据集，我们发现了四个新兴的时间组件：早上活动，夜间活动，晚上活动和中午活动。个人行为可以减少到这四个组成部分的权重。我们没有根据体重观察到任何明显的新兴人类类别，而是根据活动的时间置于连续的范围内。早晨和夜间组件的高负载与上床和醒来的时间高度相关。我们的工作指出了一种以数据为基础的数据驱动方式，该方式基于人们的全部和每周的活动和行为节奏，而不是主要关注睡眠期的时间。
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.
