Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

TL;DR

This study explores how multimodal smartphone sensors and machine learning can recognize complex daily activities across diverse countries, highlighting the importance of country-specific models for better accuracy in real-world applications.

Contribution

It introduces a 12-class complex activity recognition task and compares the performance of generic versus country-specific models across five countries.

Findings

Country-specific models outperform generic models in AUROC scores.

Multimodal sensors improve activity recognition accuracy.

Diversity-aware approaches are crucial for real-world human behavior understanding.

Abstract

Smartphones enable understanding human behavior with activity recognition to support people's daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people's daily lives and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.