DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

TL;DR

DiversityOne is a comprehensive multi-country smartphone sensor dataset with extensive demographic and psychosocial data, enabling research on cross-country human behavior modeling and model generalization in ubiquitous computing.

Contribution

It introduces one of the largest and most diverse publicly available datasets spanning eight countries with extensive sensor and self-report data, addressing previous limitations.

Findings

Includes data from 782 students across 8 countries

Contains over 350,000 self-reports and 26 sensor modalities

Enables research on cross-country behavior and model robustness

Abstract

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.