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Complex daily activities, country-level diversity, and smartphone sensing: a study in Denmark, Italy, Mongolia, Paraguay, and UK

Assi, Karim, Meegahapola, Lakmal, Droz, William, Kun, Peter, De Götzen, Amalia, Bidoglia, Miriam, Stares, Sally ORCID: 0000-0003-4697-0347, Gaskell, George, Chagnaa, Altangerel, Ganbold, Amarsanaa, Zundui, Tsolmon, Caprini, Carlo, Miorandi, Daniele, Zarza, José Luis, Hume, Alethia, Cernuzzi, Luca, Bison, Ivano, Rodas Britez, Marcelo Dario, Busso, Matteo, Chenu-Abente, Ronald, Giunchiglia, Fausto and Gatica-Perez, Daniel (2023) Complex daily activities, country-level diversity, and smartphone sensing: a study in Denmark, Italy, Mongolia, Paraguay, and UK. In: CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Conference on Human Factors in Computing Systems - Proceedings. Association for Computing Machinery. ISBN 9781450394215

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Identification Number: 10.1145/3544548.3581190

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 ∼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.

Item Type: Book Section
Additional Information: © 2023 ACM.
Divisions: Methodology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences
Date Deposited: 12 Jun 2023 13:51
Last Modified: 19 May 2024 02:21
URI: http://eprints.lse.ac.uk/id/eprint/119384

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