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Related Experiment Video

Updated: Feb 18, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Tracking urban human activity from mobile phone calling patterns.

Daniel Monsivais¹, Asim Ghosh¹, Kunal Bhattacharya¹

¹Department of Computer Science, Aalto University School of Science, Espoo, Finland.

Plos Computational Biology

|November 22, 2017

Summary

This summary is machine-generated.

Urban daily rhythms, influenced by circadian clocks, are studied using mobile phone data. Activity onset and termination synchronize with the sun

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Last Updated: Feb 18, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

Chronobiology
Urban studies
Data science

Background:

Circadian clocks regulate human activities, synchronized by environmental cues like sunlight.
Urban environments with artificial light and social cues can disrupt natural circadian entrainment.
The extent of this disruption in urban settings requires further investigation.

Purpose of the Study:

To investigate the dynamics of urban daily rhythms using large-scale mobile phone activity data.
To understand how human activity patterns synchronize with environmental signals in cities.

Main Methods:

Analysis of mobile phone calling activity from approximately 1,000,000 users across different cities within the same time zone.
Statistical analysis to infer daily and seasonal dynamics of human activity patterns.
Correlation of activity timings with solar progression and solar midnight.

Main Results:

Calling activity onset and termination synchronize with the east-west progression of the sun.
Activity timings exhibit yearly dynamics, varying with seasons and entraining to solar midnight.
Average mid-sleep time in urban populations is influenced by age and gender, reflecting biological and social factors.

Conclusions:

Large-scale mobile phone data can effectively reveal urban daily rhythms and their synchronization with solar cycles.
Urban human activity patterns are significantly influenced by both solar cues and demographic factors.
Findings highlight the complex interplay between natural environmental signals and human behavior in urbanized settings.