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

• URL: http://arxiv.org/abs/2302.08591v1
• Date: Thu, 16 Feb 2023 21:34:55 GMT
• Title: Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
• Authors: Karim Assi, Lakmal Meegahapola, William Droz, Peter Kun, Amalia de Gotzen, Miriam Bidoglia, Sally Stares, George Gaskell, Altangerel Chagnaa, Amarsanaa Ganbold, Tsolmon Zundui, Carlo Caprini, Daniele Miorandi, Alethia Hume, Jose Luis Zarza, Luca Cernuzzi, Ivano Bison, Marcelo Dario Rodas Britez, Matteo Busso, Ronald Chenu-Abente, Fausto Giunchiglia, Daniel Gatica-Perez
• Abstract summary: Smartphones enable understanding human behavior with activity recognition to support people's daily lives. People are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings. We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries.
• Score: 6.52702503779308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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