  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.
