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 [BibTeX] [Marc21]
Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries
Type of publication: Journal paper
Citation: Meegahapola_IMWUT_2022
Publication status: Accepted
Journal: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
Volume: 6
Number: 4
Year: 2022
Month: December
DOI: 10.1145/3569483
Abstract: Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. Hence, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Further, with the country-agnostic approach, we show that models do not perform well compared to country-specific settings, even when models are partially personalized. We also show that continent-specific models outperform multi-country models in the case of Europe, possibly due to the cultural similarity of European countries leading to similar phone sensor data. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical/cultural similarity of countries impacts mood inference.
Projects Idiap
Authors Meegahapola, Lakmal Buddika
Droz, William
de Götzen, Amalia
Nuttakki, Chaitanya
Diwakar, Shyam
Ruiz-Correa, Salvador
Song, Donglei
Xu, Hao
Bidoglia, Miriam
Gaskell, George
Chagnaa, Altangerel
Ganbold, Amarsanaa
Zundui, Tsolmon
Caprini, Carlo
Miorandi, Daniele
Hume, Alethia
Zarza, José Luis
Cernuzzi, Luca
Bison, Ivano
Britez, Marcelo Rodas
Busso, Matteo
Chenu-Abente, Ronald
Gunel, Can
Giunchiglia, Fausto
Schelenz, Laura
Gatica-Perez, Daniel
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  • Meegahapola_IMWUT_2022.pdf