Modeling biological rhythms helps understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms. The first step in modeling biological rhythms is to identify their characteristics, such as cyclic periods, phase, and amplitude. However, human rhythms are susceptible to external events, which cause irregular fluctuations in waveforms and affect the characterization of each rhythm. In this paper, we present our exploratory work towards developing a computational framework for automated discovery and modeling of human rhythms. We first identify cyclic periods in time series data using three different methods and test their performance on both synthetic data and real fine-grained biological data. We observe consistent periods are detected by all three methods. We then model inner cycles within each period through identifying change points to observe fluctuations in biological data that may inform the impact of external events on human rhythms. The results provide initial insights into the design of a computational framework for discovering and modeling human rhythms.

中文翻译：

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迈向从可穿戴数据流中自动发现和建模生物节律的计算框架

对生物节律进行建模有助于了解人体生理和心理异常背后的复杂原理，规划生活时间表，并避免由于这些节律不同步而导致的持续疲劳以及情绪和睡眠改变。模拟生物节律的第一步是识别它们的特征，例如循环周期、相位和幅度。然而，人的节律容易受到外部事件的影响，这会导致波形的不规则波动并影响每个节律的表征。在本文中，我们展示了我们的探索性工作，旨在开发用于人类节奏的自动发现和建模的计算框架。我们首先使用三种不同的方法识别时间序列数据中的循环周期，并在合成数据和真实的细粒度生物数据上测试它们的性能。我们观察到所有三种方法都检测到一致的时期。然后，我们通过识别变化点来模拟每个时期内的内部循环，以观察生物数据的波动，这些波动可能会告知外部事件对人类节律的影响。结果为设计用于发现和建模人类节律的计算框架提供了初步见解。