  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.
