Towards a Computational Framework for Automated Discovery and Modeling of Biological Rhythms from Wearable Data Streams

TL;DR

This paper explores a computational framework for automatically discovering and modeling human biological rhythms from wearable data, focusing on identifying cyclic periods and fluctuations influenced by external factors.

Contribution

It introduces methods to detect cyclic periods and change points in biological data, advancing automated rhythm analysis from wearable sensors.

Findings

Consistent period detection across methods on synthetic and real data

Identification of fluctuations within cycles related to external events

Initial framework for automated biological rhythm modeling

Abstract

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.