Work

Biobehavioral Rhythms in Everyday Life: Data and Models for Capturing Cyclic Behavior in Naturalistic Settings

Year: 2025

Type: article

Abstract: The ability to continuously and passively monitor human behavior using data from mobile and wearable devices in everyday environments presents new opportunities for tracking behavioral patterns that require detailed and long-term multimodal data. This paper introduces a dataset and corresponding methods for capturing and analyzing cyclical patterns of varying lengths from biobehavioral data collected passively through smartphones and wearable devices. The dataset includes up to 16 months of continuous records from smartphones and Fitbit devices, along with daily surveys from 166 university students. In addition to evaluating existing methods for modeling cyclical behavior, we also develop and present a new approach that facilitate multidimensional modeling and comparison of biobehavioral cycles within a population and across different time periods. We evaluate our methods using collected data to identify differences in cyclical behavioral patterns among various groups of students over different periods of the study.

Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

DOI: 10.1145/3770698

Authors: Chong Zhao, Maria Ana Cardei, Matthew Clark, Runze Yan, Afsaneh Doryab

Institutions: Grinnell College, University of Virginia, Emory University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CHS: Small: Computational Modeling of Human Rhythms to Improve Health and Quality of Life